Detecting, representing, and interpreting three-space input: gestural continuum subsuming freespace, proximal, and surface-contact modes

ABSTRACT

Systems and methods for detecting, representing, and interpreting three-space input are described. Embodiments of the system, in the context of an SOE, process low-level data from a plurality of sources of spatial tracking data and analyze these semantically uncorrelated spatiotemporal data and generate high-level gestural events according to dynamically configurable implicit and explicit gesture descriptions. The events produced are suitable for consumption by interactive systems, and the embodiments provide one or more mechanisms for controlling and effecting event distribution to these consumers. The embodiments further provide to the consumers of its events a facility for transforming gestural events among arbitrary spatial and semantic frames of reference.

RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Application No.61/175,374, filed May 4, 2009.

This application is a continuation in part application of U.S. patentapplication Ser. No. 12/572,689, filed Oct. 2, 2009.

This application is a continuation in part application of U.S. patentapplication Ser. No. 12/109,263, filed Apr. 24, 2008.

This application is a continuation in part application of U.S. patentapplication Ser. No. 12/553,845, filed Sep. 3, 2009.

TECHNICAL FIELD

Embodiments are described relating to gesture-based control systemsincluding the representation, manipulation, and exchange of data withinand between computing processes.

BACKGROUND

Conventional programming environments do not fully support cross-networkexecution and/or flexible sharing of data between large numbers ofcomputing processes. For example, conventional user-facing computingplatforms provide facilities for transmitting event data betweenprocesses. But these conventional mechanisms all suffer fromshortcomings that make it difficult to build multi-process andmulti-machine applications. For example, conventional event frameworksare strongly typed, which makes them inflexible, and forms a mismatchwith the facilities of increasingly popular dynamic applications. Theconventional frameworks are also configured only to supportpoint-to-point data transfers, which makes coordinating the activity ofmore than a few distinct processes difficult or impossible. Theconventional frameworks are also strongly dependent on particular local,in-memory data structures, which renders them unsuited for on-diskstorage or transmission across a network.

INCORPORATION BY REFERENCE

Each patent, patent application, and/or publication mentioned in thisspecification is herein incorporated by reference in its entirety to thesame extent as if each individual patent, patent application, and/orpublication was specifically and individually indicated to beincorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for detecting, representing, andinterpreting three-space input, under an embodiment.

FIG. 2 is a processing-centric block diagram of the system fordetecting, representing, and interpreting three-space input, under anembodiment.

FIG. 3 is an alternative block diagram of a system for detecting,representing, and interpreting three-space input, under an embodiment.

FIG. 4 is a block diagram of the gestural I/O, under an embodiment.

FIG. 5 is a data funnel of the gestural I/O, under an embodiment.

FIG. 6 is a gesture engine of the gestural I/O, under an embodiment.

FIG. 7 is a block diagram of the anonymous, asynchronous repositorydistribution mechanism of a distributor, under an embodiment.

FIG. 8 is a block diagram of the directed recipient distributionmechanism of a distributor, under an embodiment.

FIG. 9 is a block diagram of the spatial-continuum input system, underan embodiment.

FIG. 10 is a block diagram of a gestural control system, under anembodiment.

FIG. 11 is a diagram of marking tags, under an embodiment.

FIG. 12 is a diagram of poses in a gesture vocabulary, under anembodiment.

FIG. 13 is a diagram of orientation in a gesture vocabulary, under anembodiment.

FIG. 14 is a diagram of two hand combinations in a gesture vocabulary,under an embodiment.

FIG. 15 is a diagram of orientation blends in a gesture vocabulary,under an embodiment.

FIG. 16 is a flow diagram of system operation, under an embodiment.

FIG. 17A and FIG. 17B are examples of commands, under an embodiment.

FIG. 18 is a block diagram of a processing environment including datarepresentations using slawx, proteins, and pools, under an embodiment.

FIG. 19 is a block diagram of a protein, under an embodiment.

FIG. 20 is a block diagram of a descrip, under an embodiment.

FIG. 21 is a block diagram of an ingest, under an embodiment.

FIG. 22 is a block diagram of a slaw, under an embodiment.

FIG. 23A is a block diagram of a protein in a pool, under an embodiment.

FIG. 23B1 and FIG. 23B2 show a slaw header format, under an embodiment.

FIG. 23C is a flow diagram for using proteins, under an embodiment.

FIG. 23D is a flow diagram for constructing or generating proteins,under an embodiment.

FIG. 24 is a block diagram of a processing environment including dataexchange using slawx, proteins, and pools, under an embodiment.

FIG. 25 is a block diagram of a processing environment includingmultiple devices and numerous programs running on one or more of thedevices in which the Plasma constructs (i.e., pools, proteins, and slaw)are used to allow the numerous running programs to share andcollectively respond to the events generated by the devices, under anembodiment.

FIG. 26 is a block diagram of a processing environment includingmultiple devices and numerous programs running on one or more of thedevices in which the Plasma constructs (i.e., pools, proteins, and slaw)are used to allow the numerous running programs to share andcollectively respond to the events generated by the devices, under analternative embodiment.

FIG. 27 is a block diagram of a processing environment includingmultiple input devices coupled among numerous programs running on one ormore of the devices in which the Plasma constructs (i.e., pools,proteins, and slaw) are used to allow the numerous running programs toshare and collectively respond to the events generated by the inputdevices, under another alternative embodiment.

FIG. 28 is a block diagram of a processing environment includingmultiple devices coupled among numerous programs running on one or moreof the devices in which the Plasma constructs (i.e., pools, proteins,and slaw) are used to allow the numerous running programs to share andcollectively respond to the graphics events generated by the devices,under yet another alternative embodiment.

FIG. 29 is a block diagram of a processing environment includingmultiple devices coupled among numerous programs running on one or moreof the devices in which the Plasma constructs (i.e., pools, proteins,and slaw) are used to allow stateful inspection, visualization, anddebugging of the running programs, under still another alternativeembodiment.

FIG. 30 is a block diagram of a processing environment includingmultiple devices coupled among numerous programs running on one or moreof the devices in which the Plasma constructs (i.e., pools, proteins,and slaw) are used to allow influence or control the characteristics ofstate information produced and placed in that process pool, under anadditional alternative embodiment.

DETAILED DESCRIPTION

Systems and methods are described for processing low-level data from aplurality of sources of spatial tracking data. Embodiments of thesystems and methods are provided in the context of a Spatial OperatingEnvironment (SOE), described in detail below. The SOE, which includes agestural control system, or gesture-based control system, canalternatively be referred to as a Spatial User Interface (SUI) or aSpatial Interface (SI).

FIG. 1 is a block diagram of a system 10 for detecting, representing,and interpreting three-space input, under an embodiment. Embodiments ofthe system 10, in the context of the SOE 5, process low-level data 1from a plurality of sources of spatial tracking data and analyze thesesemantically uncorrelated spatiotemporal data and generate high-levelgestural events 3 according to a set of dynamically configurableimplicit and explicit gesture descriptions 2. The events 3 produced aresuitable for consumption by interactive systems (not shown), and theembodiments provide one or more mechanisms 4 for controlling andeffecting event distribution to these consumers. The embodiments furtherprovide to the consumers of its events 3 a facility for transforminggestural events among arbitrary spatial and semantic frames ofreference.

Central to the embodiments herein is the assertion that the conceptualdomain of gesture is a spatial and semantic continuum 6. At one end ofthe continuum 6 is fully unconstrained freespace gestural input 6A, inwhich one or more hands cooperate to describe curvilinear trajectoriesthrough three dimensional space and in which, simultaneously, aggregatefinger poses evolve over time. At the other end is surface-contact input6D, in which one or more fingers are “constrained” to lie on a one- ortwo-dimensional manifold (literature often refers to this form as“touch-based input”). Between these extremes is an elaboration of touchthat may be termed “hover input” 6C; here, the fingers remain close to amanifold but are not in contact with it; such relaxation of the contactrequirement allows for additional degrees of freedom to be deployed.More generally, it is useful to speak of “proximal input” 6B, in whichgesture occurs in a range of defined proximity to one or more surfaces,or is restricted to a particular volume. It is evident that eachgestural “category” shades into the next—from freespace 6A, to proximal6B, to hover 6C, to touch 6D—and that, moreover, each such categoryproperly, formally, and geometrically subsumes the next. It will beunderstood as well that this continuum 6 of “gestural input” is by nomeans restricted to human hands: tagged or otherwise trackable physicalobjects are also valid participants in the input continuum 6.

The embodiments herein make explicit a distinction between two ways inwhich the points along the input continuum 6 may be considered. From thevantage of sensing, different input mechanisms appear to subscribe todifferent regions of the continuum 6: a high-fidelity motion-capturerig, for example, seems to provide six-degree-of-freedom freespace input6A, while an electric-field-sensing apparatus seems to generatehover-style input 6C, and a typical capacitive sensing unit seems toreport touch input 6D. From the vantage of event consumption—and thusfrom the vantage of semantics—the low-level origin of an event ought tobe of little interest; and in fact it is often of great utility to beable to apprehend the same event as rendered into differentrepresentations (e.g. as a freespace gesture, and also as a hovergesture). However, prior work has tended to conflate the two vantages.That is, other systems typically regard a touchscreen surface asnecessarily and solely generating two-dimensional touch events, forexample.

It is one advance of the embodiments described herein, contrariwise, tomaintain the distinction between the two vantages. FIG. 2 is aprocessing-centric block diagram of the system 10 for detecting,representing, and interpreting three-space input, under an embodiment. Afirst stage 11 of an embodiment collates low-level input from adisparate collection of sources and conforms the low-level eventsvariously produced into a single stream of uniformly representedspatiotemporal data. A second stage 12 parses the conformed low-leveldata into semantically significant gestural events and represents thesein a neutral but fully articulated form. A third stage 13 distributesthe resulting neutral events to consumers, and provides facilities bywhich consumers may transform any event into a locally optimal semanticform. So, for example, an embodiment uses per-finger high-fidelitysix-degree-of-freedom input to produce touch events with reference to atable surface; in this case, the surface is itself uninstrumented, butis instead represented mathematically, as a geometric structure—so that,absent specialized touch-sensing hardware, touch may still be deduced:computationally, via geometric intersection. In short, the formalisms ofthe embodiments enable the fully general exercise of variegated spatialinput.

A description follows of the embodiments, the description comprising (1)a larger context for the embodiments: a typical ecology of systems inwhich the embodiment plays a crucial role; (2) a summary of the threepipeline-like components comprising the embodiments; (3) detaileddescriptions of the three components, each with occasional illustrativeexamples; (4) a full implementation of the pipeline's second component;and (5) four scenarios illustrating different interactive systemsenabled by the embodiments.

In the following description, numerous specific details are introducedto provide a thorough understanding of, and enabling description for,embodiments described herein. One skilled in the relevant art, however,will recognize that these embodiments can be practiced without one ormore of the specific details, or with other components, systems, etc. Inother instances, well-known structures or operations are not shown, orare not described in detail, to avoid obscuring aspects of the disclosedembodiments.

The following terms are intended to have the following general meaningas they are used herein. The term “processes” as used herein meansseparable program execution contexts. Computer architectures andoperating systems differ in the technical details of processimplementation. The mechanism described here is configured to operateacross a broad range of process implementations and to facilitate hybridapplication designs or configurations that take advantage of as manyavailable computing resources as possible.

The term “device” as used herein means any processor-based devicerunning one or more programs or algorithms, any processor-based devicerunning under one or more programs or algorithms and/or any devicecoupled or connected to a processor-based device running one or moreprograms or algorithms and/or running under one or more programs oralgorithms. The term “event” as used herein means any event associatedwith a running or executing program or algorithm, a processor-baseddevice and/or a device coupled or connected to a processor-based device(e.g., an event can include, but is not limited to, an input, an output,a control, a state, a state change, an action, data (regardless offormat of the data or stage in the processing from with which the datais associated), etc.).

Embodiments of the systems and methods are provided in the context of aSpatial Operating Environment (SOE), as described above. An SOE is acomplete application development and execution platform and is analogousin some ways to an operating system. An SOE however privileges bothreal-world three-dimensional geometries and efficient, high-bandwidthinteractions between computer and human operator, and thus implements asophisticated interface scheme. In turn, the SOE replaces manytraditional OS services and architectures—which are inadequate to therequirements of such rich, nuanced interface—with new low- andmedium-level system infrastructures.

FIG. 3 is an alternative block diagram of a system 20 for detecting,representing, and interpreting three-space input, under an embodiment.The system 20 is operating in the context of the SOE 5. The majorcomponents of the SOE 5 are the gestural I/O 14, network-based datarepresentation, transit, and interchange 15, and a spatially conformeddisplay mesh 16. Each of the components of the SOE 5 is described indetail below.

In describing the gestural I/O 14 of an embodiment, the combinatoricimplications of the human hand—its bulk position and orientation, alongwith the “pose” formed by the aggregate of its fingers' flexions—and thefine motor control enjoyed by most humans together make hand-basedgesture the crucial external component in the SOE input system. An SOE 5thus tracks hands with high fidelity throughout a threespace volume.Other subordinate objects (e.g., physical and often graspable “tools”for channeling or manipulating digital content) may also be tracked.Gestural interactions are most often undertaken with reference todynamic entities depicted on two- and three-dimensional displaysoperating in the visual, aural, and haptic domains. Active feedback“glyphs” make simultaneous use of the SOE's displays in order to (a)apprise operators of the system's instantaneous and ongoinginterpretation of gestural input; (b) enumerate possible gestural “nextsteps”, based on system state and on local gestural history; and (c)provide a sketchlike “preview” of the imminent manipulatory consequencesof a gestural sequence.

Structurally, the input portion of the SOE's gestural I/O 14 systemtakes the form of an approximately linear pipeline. At the earlieststage, the pipeline acts to process, correlate, and seam spatialtracking information from a possible plurality of sources including anynumber, type, and/or combination of data streams/sources SY (where Y isany number 1, 2, . . . ); and subsequently to collect individualelements into aggregates of known configuration and desirability (e.g.fingers considered at first separately are collected into a full handrepresentation). The pipeline's second stage is a gesture engine thatinterprets the results of the first stage and attempts to detect anddisambiguate gestural occurrences. In the third stage, “events” ofmedium-level representation are passed to event consumers, which maymake use of SOE facilities for transforming those generic events intoforms geometrically relevant to local circumstance.

The network-based data representation, transit, and interchange 15 of anembodiment includes a system called “Plasma” that comprises subsystems“slawx”, “proteins”, and “pools”, as described in detail below. Slawx(plural of “slaw”) are self-describing data constructs that encompassatomic forms—strings and an expansive collection of numeric types,including elemental support for complex, vector, Clifford (or“multivector”), and array entities—as well as arbitrarily nestableaggregate forms—“cons” dyads, heterogeneous lists, and unique-keyedassociation lists. Proteins are prescribed-structure encapsulations ofmultiple slawx: an arbitrary-length concatenation of slawx (usuallystrings) called “descrips” provides a conveniently searchabledescription of a protein; while an arbitrary-length concatenation ofkey-value cons dyads, called “ingests”, forms a protein's data payload.In an embodiment, proteins are themselves a particular species of slaw.Pools are persistent, linear-sequential collections of proteins;arbitrarily many processes may connect in parallel to a given pool. Eachconnected process may deposit proteins into the pool, retrieve proteinsfrom the pool, or both. Low-level pool mechanisms ensure that pooltransactions on a local machine and those undertaken remotely (over anetwork) are, from the programmer's and the executing code's point ofview, indistinguishable. Retrieval of a protein deposited by a distantprocess automatically conforms all encapsulated slawx, so that hardware-and architecture-specific data format differences (endianness, e.g.) areinvisibly resolved. Pools are of conceptually infinite capacity &temporal duration, so that a process may at any time “rewind” backwardthrough the pool's history, accessing older and older proteins.Implementations of Plasma are exceedingly optimized; pool-mediatedproteins thus form a highly desirable representation-mechanism forinterface events, system events, interprocess messaging, streaming ofhigh-density media, exchange of structured data, and so on. Further, theprovisions of the Plasma system enable and encourage construction ofcomplex “applications” as ecologies of independent, modular processescoordinated through protein interchange.

The SOE 5 of an embodiment, as described above, includes a spatiallyconformed display mesh 16. A central premise of the SOE 5 is thatexternalized manifestations of a computational process—the visual,aural, and haptic displays through which a process expresses its stateand represents information—must conform themselves logically to thereal-world space in which they are physically embedded. Thus the SOE 5provides at every programmatic level a system of basic constructs forthe description and manipulation of three-dimensional geometry.

Geometry is always described in a “real-world” coordinate frame, suchcoordinates being deliberately appropriate to the description of theroom or space in which the SOE 5 is resident. So, for example, anytwo-dimensional visual display (a monitor, say) controlled by the SOE 5maintains not only a description of its pixel resolution but also of itsphysical size, location, and orientation in the room. This means thatindividual pixels on the display have real-world locations and extents;and that, similarly, graphical constructs displayed on the device arepossessed of authentic physical (room-conformed) geometry. Thisgeometry-based representation scheme has immediate, substantial importbecause the same geometry and coordinate system is employed by the SOE'sinput system. In consequence, the SOE 5 can provide co-located input andoutput. When an operator points from a distance at a graphical objectdisplayed on a screen, the system is able logically to consider that sheand the graphics are present—with knowable geometric relationship—in thesame threespace continuum. The intersection calculation that determineswhat is being pointed at is thus mathematically trivial, and thegraphical object may then immediately react or subject itself to theoperator's manipulations. The resulting spatial causality leads in turnto the operator's perceptual and cognitive conviction that the graphicsare in the room with her; and, in every relevant sense, such aconviction is accurate. The expectations and modalities induced bycurrently dominant human/machine interfaces thereby undergo a valuableinversion, and a paradigm of “direct spatial manipulation” obtains.

The SOE 5 provides additional facilities for geometrically relatingdisjoint spaces (as, for example, with a telecollaboration system that“seams” two or more separate interaction sites across privileged visualdisplays) and for converting geometric constructs to allowinterpretation in different local reference frames. Finally, the SOE 5provides legible representations for “reduced geometries”, that is,logical relationships among data that cannot meaningfully be understoodvia connected-space (i.e. Euclidean, Minkowski, anti de Sitter, etc.)forms; here, the SOE offers basic topologic representation.

The embodiments described herein form the major part of the input sideof the gestural I/O system 14 of the SOE 5. The embodiments can beviewed as analogous to a pipeline that transforms very low-level(semantically: “signal level”) input into much more structured,symbolic, and context-specific input for consumption by, say,higher-level SOE components. This is not however to say that theembodiments operate in an unstructured, pure-literal, orcontext-impoverished mode: much of the crucial efficacy of the pipelinederives from its impediment-free access to high-level geometric andcomputational context belonging to other of the SOE's component systems.

FIG. 4 is a block diagram of the gestural I/O 14, under an embodiment.In summary, the earliest stage 20 of the gestural I/O 14—a conceptual“data funnel” 20—acts to process, correlate, and seam spatial trackinginformation from a possible plurality of sources. For example, an SOE 5of which the pipeline of an embodiment is a part may make simultaneous,coordinated use of (a) several motion-tracking devices serving distinctvolumes, (b) constrained-purview machine vision tracking in the vicinityof individual workstations, and (c) electric-field-analysis proximityand touch sensing associated with a large projection table. The funnel20 renders low-level spatial events from any number, type, and/orcombination of data streams/sources SY (where Y is any number 1, 2, . .. ) in a conformed-coordinate representation (with reference to theglobal room space). Immediately thereafter the funnel 20 generates,where appropriate, logical aggregates expressing both literal geometricand semantic characteristics (a hand whose fingers are individuallytagged gives rise at this stage to a description as a high-precisionoverall position and orientation together with a compact notation ofdactylic pose).

These elemental events are passed to the input system's second stage, a“gesture engine” 21 whose work is to detect and disambiguate particularspatiotemporal circumstances—“gestures”—that may be of interest toindividual processes, active computational objects, system-widenotification constructs, and so on. Activities of the gesture engine 21are guided by a set of spatiotemporal rules—descriptions of particulargestures or classes of gestures—that may be statically or dynamicallyconfigured. The engine produces detailed but neutrally descriptive databundles (“protoevents”) articulating the detected gesturalcircumstances.

Finally, the third stage 22 of the gestural I/O 14 distributesprotoevents emitted by the gesture engine to such event-consumingmechanisms as may be in programmatic contact with it. Each eventconsumer has access to a facility provided by the third stage that canre-render a protoevent bundle “in local terms”: that is, can re-expressthe event in spatial-semantic form relative to a particular localgeometry. For example, a hand thrust toward a screen with index and ringfingers forming the V of a “victory symbol” may be rendered as asingular postural configuration at a precise threespace room location;or as an overall hand-proximity condition with respect to the screen; oras a constellation of near-touch events in which each finger isconsidered separately.

FIG. 5 is a data funnel 20 of the gestural I/O 14, under an embodiment.The data funnel 20, also referred to herein as the input funnel 20,transforms low-level spatial input data 1 (from a semantic point ofview, “signal level”) into a time-resolved stream ofgesture-engine-ready (GER) data 27 to be fed to the pipeline's secondstage, the gesture engine 21. The transformation executed by the datafunnel 20 comprises collecting, temporally aligning 23, and spatiallyseaming 24 the low-level input data to form a single synthetic(“conformed”) data stream. Subsequently, the funnel acts to identifyprivileged subsets of the conformed data and assemble 25 each subsetinto a reduced-entropy semantic aggregate.

The funnel receives as input one or more spatiotemporal data streams SY(where Y is any number 1, 2, . . . ). The data streams SY may inherentlyrepresent different degree-of-freedom counts: an optical motion-trackingsystem can typically resolve appropriately tagged fingers, with highfidelity, through all six degrees of freedom (three translational andthree rotational); a time-of-flight-based camera supplies, based on theanalysis method used, either three-, five-, or six-degree-of-freedomdata about a hand's digits; an electric field sensing rig may providethree DOF information describing position of a hand's overall mass, withresolution differentially dependent on position; a touch screen may emittwo-dimensional tracking information subject to physical contactconstraints; and so on. The data streams SY may provide individualspatiotemporal events at differing rates. The data streams SY may beintermittent, as for example when tracked hands or other objects enterand leave the volume treated by a sensing mechanism.

The data streams SY include, where available, estimates of the accuracyor likely range of error of the spatial and temporal quantitiesrepresented. For example, the data stream from an electric field sensingrig may annotate each event with an assessment of the spatial errorwhich, for such a device, not only differs along the local x and y(“planar”) axes versus along the local z (“distance”) axis but alsovaries in overall magnitude as a function of the true spatial position.Such accuracy annotations may be a “received” element of the data stream(if the device itself or the device drivers are capable of providing it)or may be deduced by the funnel's early processing (in cases where itmaintains a model of the originating device's operation).

A component of the funnel 20 temporally aligns 23 a plurality of datastreams SY. The funnel 20 may be configured to accomplish such alignmentin several distinct ways. Alignment schemes 23 include but are notlimited to the following: (1) interpolation provides “virtual”spatiotemporal events from all other data streams at every “real”temporal event instance from one or more data streams; (2) interpolationprovides, at each temporal event instance in the stream whose data rateis the highest, virtual events from each of the other streams, the otherstream's “real” events being discarded; (3) as with the foregoing, butwith an explicitly designated stream used as the “ticking metronome” towhich all other streams are aligned; (4) the foregoing, but with anexternally imposed metronomic tick coinciding with none of the streams,so that all streams are interpolated. The result of the temporalalignment 23 is a data stream for which, at each timestep, a possiblemultiplicity of representational events is emitted, the per-timestepaggregate offering possibly alternate interpretations of the same“objective” (real-world) event. Each resulting post-alignment eventincludes, where possible, a representation of its unique identity (e.g.a particular finger or object, when appropriately tagged or reliablydeduced). Such identity information is useful to subsequent processing,as when a single spatial event must be synthesized from alternaterepresentations of the same real-world event. Where the operation oftemporal alignment 23 changes the estimated error or accuracy range ofcomponent degrees of freedom, events are tagged accordingly.

The funnel 20 of an embodiment spatially seams 24 events from theplurality of data streams. The spatial seaming 24 often but not alwayspresupposes prior temporal alignment of identity-tagged events. Spatialseaming 24 generally requires the promotion of each contributing eventto the highest possible level of description. Where such descriptionpromotion changes the estimated error or accuracy range of componentdegrees of freedom, events are tagged accordingly. Degrees of freedomfor which such promotion is impossible are explicitly tagged. In somecases, this circumstance corresponds functionally or explicitly to aninfinite error range. Description promotion may simply entail thatparticipating events are re-rendered into a conformed spatial referenceframe (as necessary where the data streams initially represent spatialevents in local frames). This in turn requires that the funnel maintainor have access to a conception of the relationship of each local framewith respect to the universal (“room”) frame. Thus, for example, a touchevent from a contact-sensing surface, represented initially in the local(x′, y′) frame of the surface, is transformed using the known physicalgeometry of the surface into the (x, y, z) frame of the room; the threerotational degrees of freedom are in that case tagged as unknowable,since they cannot be deduced from the device's data stream. Alternate,more complex methods of description promotion, including those relyingon inference and deduction techniques, can also be used.

Subsequently, spatial seaming 24 produces, for each aggregate ofalternate descriptions of the same real-world event, a single“synthetic” event (taken to be the real-world event's most accuraterepresentation). Synthesis methods include, but are not limited to, (1)selecting a single description from among the plurality of input datastreams and discarding the rest—the synthetic event is “winner takeall”; (2) for each promoted-description degree of freedom, selecting thecorresponding component data from a single description and discardingthe rest—the synthetic event is componentwise “winner take all”; (3)performing a weighted average of each degree-of-freedom component acrossall descriptions, the weights determined by configurable andcontextually sensitive functions; (4) permutations of (2) and (3). Thecriteria by which the method of synthesis is chosen may be implicitly orexternally fixed, or may be statically or dynamically configured torespond to context.

In an example a volume is “treated” by a collection of identicalsensors, each one of which has finite range, and each of whose accuracydegrades as its sensing range's edge is approached, and which arespatially arranged so that their sensing ranges overlap. Spatial seamingmay select a single description when the event in question is wellinside a single sensor's high-precision range, but may then perform aweighted average between adjacent sensors' streams when the event occursnear the range limit of the first sensor. The weighting variesspatially, in response to the event's estimated proximity to respectivesensing boundaries.

In a second example event streams represent a high-fidelity opticalmotion tracker and a touch surface. Spatial seaming generally favors themotion tracker, but as tracking approaches the touch surface, anadjustment function is applied to the optical location data so thatdistance from the touch surface decreases asymptotically. Only when thetouch surface senses definitive contact is the seamed event's locationallowed to coincide geometrically and semantically with the surface.

In a final example a display-backed surface is outfitted with ahigh-precision electric-field-sensing apparatus, and a pair of cameraswith stereo depth processing is trained on the surface. Thefield-sensing rig provides better resolved location data for a fingernear the display (than does the vision system), but field sensing'sability to detect orientation is negligible, so spatial seaming mergesthe three location components from one sensor with the three orientationcomponents of the other, resulting in a synthetic event that exhibitsgood resolution in all six degrees of freedom.

It is an explicitly configurable or contextually triggerable aspect ofthe funnel to allow spatial seaming 24 to precede temporal alignment 23;this may happen continuously or intermittently. For example, the funnel20 may be configured so that input streams SY are ordinarily alignedagainst the highest-data-rate stream but that, upon detection of anextraordinary event (a finger crossing a proximal threshold, say), a“syncopated” aggregate event is generated by interpolating all otherstreams to the time of detection.

The funnel 20 of an embodiment also performs semantic aggregation 25,which includes collecting relevant events resulting from precedingfunnel operations into semantic aggregates. The manner in which orpatterns by which such aggregate collection 25 happens may be staticallyor dynamically configured. The aggregates that the funnel may beconfigured to produce at this stage are typically, though not always,(1) explicitly specified, so that their identification and assembly is adirect and causal matter subject to no sophisticated inference; and (2)of universal “downstream” utility. An extremely pervasive exampleattends the identification of a human hand assembly: for an inputinfrastructure in which individual fingers are tagged so that both thesix-DOF geometry as well as the identity of each finger are reliablyreported, the component elements of the hand may be a priori prescribed.The act of forming the higher-level semantic hand aggregate is thensimply a matter of selecting from the conformed input stream those tagswhose identities match the static identities known to comprise the hand.

Note that even in this example—in which the possibility of assemblingthe aggregate is guaranteed, so long as the component tags are reportedin the input stream—the output stream would be likely to include notonly the resulting high-level representation but also the lower-leveltag information from which the aggregate had been assembled. Subsequentconsumers of the event information are thus afforded the possibility ofaccessing the lower-level data when and as necessary (see immediatelybelow).

Additionally, the funnel 20 can perform metainformation tagging 26during one or more operations described above. When metainformationtagging 26 is used at or as part of any operation described above, theresulting events bear information pertaining to their construction,including a complete or abridged list of original events from which theywere derived, decision paths that led to particular synthesis methods,and so forth. Subsequent consumers may then elect to traverse thismetainformation in order to reinterpret or further analyze thesesynthetic events.

FIG. 6 is a gesture engine 21 of the gestural I/O 14, under anembodiment. The gesture engine 21 translates a body of low-level,semantically raw data (“gesture-engine-ready data” or GER data 27)representing spatial and geometric occurrences into one or morerepresentationally typed gestural protoevents 3. The GER data 27 of anembodiment includes, but is not limited to, the following: (1) thethreespace position and, possibly, orientation of a single finger; (2)the overall “bulk” threespace position and orientation of an entirehand, together with a semantic digest of the hand's pose—i.e. itsfingers' aggregate flexions; (3) the threespace position and orientationof an inert, non-biological object; (4) the threespace position andorientation of other anatomically germane structures, such as anoperator's head to name one example.

The gesture engine 21 consults a possible plurality of distinctgesture-describing criteria and attempts to match the various spatialGER data 27 against these criteria. As a result of the matchingexercise, zero, some, or all of the criteria will have been met; foreach match, zero or more of the GER data 27 will have been implicated.The gesture engine 21 may be configured to treat the GER data 27“exclusively”, so that a datum implicated in one match may not thenparticipate in satisfying a second, or the gesture engine 21 may insteadallow a datum to participate in multiple matches. In response to eachpositive match, the gesture engine 21 prepares zero or more“protoevents” 3: these provide a digest of the matched low-level data,interpreted in the semantic context of the matched gestural criteria.The protoevents 3 are passed along to the third stage of the pipeline,as described below.

The gesture engine 21 can comprise a logically hermetic execution path,in which are resident either a fixed and immutable set of gesturerecognition criteria or a finite set of selectable and configurablegesture recognition criteria (this selection and configuration to beeffected from outside the engine's logical boundary). But in anembodiment, each recognition criterion exists as a logically independentunit called a “recognizer”; recognizers may be selected from a library(not shown) and may be authored independently from the gesture engine21. In this embodiment, an external agency selects and configures one ormore recognizers and then brings each into data-structural associationwith the gesture engine 21. This may be done once, prior to the gestureengine's engagement, or the external agency may dynamically add 28,remove 29, and/or modify or reconfigure (not shown) recognizers duringthe gesture engine's active execution. Note too that the embodimentallows for the gesture engine 21—in response to certainconditions—itself to add 28, remove 29, and/or modify or reconfigure(not shown) recognizers in association with it. It is further possiblefor a recognizer to remove 29 and/or reconfigure itself, or to add 28,remove 29, or reconfigure other recognizers in association with the samegesture engine 21.

The GER data body 27 may in rare circumstances be temporally solitary sothat the gesture engine's action is undertaken only once, but is mostusually time-varying and presented to the gesture engine 21periodically. In this latter case the input data 27 are most oftenpossessed of persistent identity, so that it is possible for recognizersto knowably associate the geometric information represented by a datumD_i at time T_n with that of datum D_j at time T_n+1: D_i and D_jrepresent the same real-world object moving (and possibly deforming)through space. Throughout the remainder of this description, it will beunderstood that “GER datum” refers to the ongoing evolution of thosetime-sequential data bearing the same identity information, i.e.referring to the same real-world object. In an embodiment, therecognizers maintain internal state in order to represent aspects of thespatiotemporal trajectories of input data of interest.

In one case, a recognizer remains expectantly “dormant” until thegeometric and spatiotemporal circumstances of one or more input data 27match the recognizer's specific “activation” criteria, whereupon itbecomes “active”. The recognizer remains active so long as input data 27satisfy a second set of “maintenance” criteria (which may or may not beidentical to the activation criteria). The recognizer becomes inactivewhen the input data 27 fails to satisfy the second set of criteria.

Natural categories for recognizers, and thus for recognizable gestures,emerge from consideration of (1) the different forms that activation andmaintenance criteria can take, and (2) the circumstances under whichprotoevents are emitted from the gesture engine.

When the gesture engine 21 is configured to treat GER data 27exclusively, the inclusion of a datum by one recognizer in a successfulinitial match disallows the use of that datum by any other recognizer.In this way, a recognizer can “capture” one or more GER data 27, andthroughout the interval in which the recognizer is active those captureddata remain continuously unavailable for consideration by otherrecognizers.

In an embodiment a gesture engine 21 may rank its associated recognizersaccording to a “primacy metric”. Such metrics may be static throughoutthe existence of the gesture engine 21; may be volitionally modified orreplaced at discrete intervals by agencies external to the gestureengine 21; or may be automatically and dynamically evolved, discretelyor continuously, as the gesture engine 21 executes. In all such cases,the gesture engine 21 gives consideration to its plurality ofrecognizers in the order suggested by the primacy metric's ranking; andwhen the gesture engine 21 is so configured and, additionally, disposedto treat input data exclusively, it is therefore possible forhigher-ranking recognizers to “usurp” input data previously captured byother, lower-ranking recognizers. In this event, the recognizer whosedata have been usurped is notified and given the opportunity to returnto an inactive state, emitting any protoevents as may be necessary todescribe the forced state change.

For illustrative purposes, the implementation of a gesture engine andits recognizers is articulated in full detail herein.

FIG. 7 and FIG. 8 show a distributor 22 of the gestural I/O 14, underdifferent embodiments. The “distributor” 22 of an embodiment transmitsthe protoevents 3 generated by previous pipeline activity to one or morenext-stage recipients. A major class of the protoevents 3 thustransmitted comprises gestural events detected by the gesture engine,but the distributor 22 may be configured to transmit, in addition, thoselower-level events that did not participate in the detection andsynthesis of “well-formed gestures”. Additional facilities of thedistributor 22, available to event recipients and other downstreamsystems, allow transmitted protoevents 3 to be re-interpreted in(transfoinied into) specific geometric and semantic form.

Mechanisms for event distribution are varied, and the distributor may bestatically or dynamically directed to engage with an arbitrarycollection of these. The distribution mechanisms of an embodimentinclude, but are not limited to, the following: anonymous, asynchronousrepository; directed, asynchronous recipient; and, directed, synchronousrecipient. A description of the distribution mechanisms follows.

FIG. 7 is a block diagram of the anonymous, asynchronous repositorydistribution mechanism of a distributor 22, under an embodiment. Underthe anonymous, asynchronous repository distribution mechanism, thedistributor 22 exercises its connection to one or more repositories 30that may have couplings or connections to some number of auditors 31.The distributor 22 deposits protoevents 3 in these repositories 30; theprotoevents 3 are subsequently retrieved by interested auditors 31. Suchrepositories 30 may exist in the same execution space as the distributor22 and support proximal or disjoint connections from auditors 31; or mayexist as separate processes on the same hardware and support connectionsfrom the distributor 22 and from auditors 31 via interprocesscommunication protocols; or may exist as processes on remote hardwareand support connections from the distributor 22 and auditors 31 over anetwork; or may exist with properties permuted from those of theforegoing. Common to this distribution pattern is that the distributor22 need not (and in many cases cannot) be aware of the number and natureof the auditors 31. An embodiment implements such repositories throughthe provisions of proteins and pools, as described in detail below.

FIG. 8 is a block diagram of the directed recipient distributionmechanism of a distributor 22, under an embodiment. When the distributor22 includes or executes the directed, asynchronous recipientdistribution mechanism, the distributor 22 maintains an auditor list 32comprising a list of asynchronous auditors 33; the population of theauditor list 32 is controlled statically or dynamically. The distributor22 transmits to each asynchronous auditor 33 a copy of every generatedprotoevent 3, such transmission undertaken in an asynchronous modality,so that receipt acknowledgment from asynchronous auditors 33 is notnecessary. Notionally, this model of asynchronous consumption isanalogous to the message-passing “mailbox” communications offered by theErlang programming language. An embodiment implements such asynchronousconsumption through the provisions of mutex-protected shared memorymethods.

When the distributor 22 includes or executes the directed, synchronousrecipient distribution mechanism (with continued reference to FIG. 8),the distributor 22 maintains an auditor list 32 comprising a list ofsynchronous auditors 33; the population of the auditor list 32 iscontrolled statically or dynamically. The distributor 22 transmits toeach synchronous auditor 33 a copy of every generated protoevent 3, suchtransmission occurring synchronously, so that receipt of events by thedistributor's synchronous auditors 33 is implicitly or explicitlyacknowledged in bounded programmatic time. The simplest implementationof such synchronous consumption can obtain when consumers are present inthe same execution space as the distributor 22; then, transmission ofprotoevents 3 can be accomplished using a direct function call. Forcircumstances in which consumers are disjunct from the distributorprocess, techniques of interprocess communication may be employed toimplement synchronous transmission.

Independently from its event transmission or distribution activities,and with reference to FIGS. 7 and 8, the distributor 22 includes andmakes available facilities for event transformation 34. Any number ofsupplicant entities SE may communicate such event transformationrequests to the distributor 22 by any of the means articulated above,synchronous and asynchronous, and in the case that a supplicant entitySE is itself also an auditor 33 such event transformation requests arenot required to employ the same communication means as that of audition.An event submitted for transformation may have originated from thedistributor 22 (e.g., a protoevent 3), or may represent spatiotemporaldata synthesized or acquired externally to the distributor's activities.In the former case, the supplicant entity SE may elect to “retransmit”the event to the distributor 22, passing the event in full literaldetail, or may instead pass a reference to the event—a unique identifierassociated with the event—by means of which it may be retrieved by thedistributor 22.

Supplicant entities SE may request simple geometric eventtransformation, in which the coordinate system underlying the event issubjected to an affine transformation. Such transformation will ingeneral result not only in a change to the numerical representation ofthe event's geometry (i.e. a change of coordinate-based elements) butalso of certain parts of its semantic content.

So, for example, a protoevent E represented as

E: [[ DESCRIPS: :event, :pointing, :manus, 3, :evt-grp-qid, 12831 //INGESTS: :gripe => ″{circumflex over ( )}{circumflex over ( )}∥-:-x″,:pos => v3(−200.0|+1000.0| +500.0), :aim => v3(+0.35|+0.00|−0.94) ... }]]can be subjected to a ninety degree rotation about the y-axis and adownward translation (equivalent to the representation of the geometryin a coordinate system that is y-rotated by negative ninety degrees andtranslated upward from the original coordinate system) to yield

E --> E′: [[ DESCRIPS: :event, :pointing, :manus, 3, :evt-grp-qid,12831.1 // INGESTS: :gripe => ″{circumflex over ( )}{circumflex over( )}∥-:.-″, :pos => v3(+500.0|+0.0| 200.0), :aim =>v3(−0.94|+0.00|−0.35) ... } ]].Note in this case that the GRIPE string (described herein) that is asemantic digest of a hand's overall finger-postural configuration andaggregate orientation has also changed: the final two characters,designating basic orientation, have been transformed to “.-” (from“-x”).

More complex event transformations executed by the distributor 22involve the reinterpretation of some combination of a protoevent'sgeometric and semantic content in a new context. The same protoevent Eabove—an example of an apparent “pointing” gesture in which a hand'sindex and middle fingers are extended, the thumb is vertically disposed,and the ring and pinkie fingers are curled in—might be reinterpreted inthe local geometric context of a proximal display screen positioned justin front of the hand:

E --> {E1′, E2′} E1′: [[ DESCRIPS: :event, :proxing, :manus, 3, :dactyl,:middle, :evt- grp-qid, 12831.2 // INGESTS: :pos => v3(−203.0|+1000.0|+386.0), :proximals => { {(:phys-surf . 0x3dd310), (:dist . 15.4)}, ...} ]] E2′: [[ DESCRIPS: :event, :proxing, :manus, 3, :dactyl, :index,:evt- grp-qid, 12831.3 // INGESTS: :pos => v3(−203.0|+1000.0| +393.0),:proximals => { {(:phys-surf . 0x3dd310), (:dist . 22.4)}, ... } ]]

With reference to the embodiments described above, numerous examples ofgesture engine implementations follow. FIG. 9 is a block diagram of agesture engine implementation 900, under an embodiment. The followinggesture engine implementations suppose a number of principal elements,and a description of each of these principal elements follows withreference to the gesture engine implementation 900.

A first principal element of the following gesture engine implementationexamples is the presence of some number of tracked entities, therepresentation of each comprising (a) a high-fidelity bulk threespaceposition; (b) a high-fidelity bulk threespace orientation; and (c) anexpressive description of the entity's “pose”, i.e. a semisemanticdigest of its additional degrees of freedom. Call such entities“GripeEnts”. (A GripeEnt corresponds to the general “GER datum” above.)A particularly important species of GripeEnt is the human hand, forwhich “pose” describes the fingers' various flexions, expressed possiblyusing the representational schema articulated in U.S. patent applicationSer. No. 11/35069.

A second principal element of the following gesture engineimplementation examples is a system for correlating low-level spatialinput data from one or more sources and for analyzing that data in orderto periodically update the collection of GripeEnts. This system isreferred to herein as a “GripeRefinery”, where a GripeRefinery maycorrespond to the “data funnel” described above. To provide perceptuallysatisfactory interaction, the GripeRefinery must produce complete outputsets at rates well better than thirty Hertz.

A third principal element of the following gesture engine implementationexamples is the inclusion of a collection of gesture-matching modulesreferred to as “GestatorTots” or “GTots” (a GTot corresponds to thegeneral “recognizer” above, but the embodiment is not so limited). EachGTot has, at any instant, a “multiplicity” S: the coordination of Sdistinct GripeEnts is required for successful recognition of the gesturethat the GTot represents. Each GTot is at any moment in either a“waiting” or an “active” state. Associated with these states are aGTot's two major execution paths: respectively, “EntranceAttempt” and“ContinuationAttempt”, either or both of which may emit mid-levelinteraction event data. A third, optional, execution path is the GTot's“Update” routine, which provides a per-loop opportunity for the GTot toperform additional computation necessary to maintenance of its internalstate. Execution of EntranceAttempt will produce one of three possibleresult codes: COPACETIC, PROMOTE, and EXCLUSIVE. Execution ofContinuationAttempt will produce one of three possible result codes:COPACETIC, DEMOTE, and EXCLUSIVE.

An EntranceAttempt tests, for a GTot in the waiting state, availableGripeEnts against the GTot's particular entrance criteria; when thosecriteria are met, the GTot is placed in the active state and the (one ormore) GripeEnts that have participated in meeting the criteria aremarked as captured and associated with the GTot. For a GTot in theactive state, ContinuationAttempt first acts to verify that thepreviously captured GripeEnts are (1) still available—that is, if theyhave not been “usurped” by a GTot of higher primacy—and (2) stillspatially, semantically, and contextually satisfy the GTot's criteria.If (1) or (2) is not the case, the formerly captured GripeEnts arereleased from association with the GTot; otherwise, they remaincaptured.

These logical relationships and causalities are explicated in detail inthe state transition descriptions below.

A fourth principal element of the following gesture engineimplementation examples is the inclusion of an engine of arbitrationthat traverses the full set of GTots in a prescribed order and allowseach to execute either its EntranceAttempt or ContinuationAttemptroutine. This arbitration engine is referred to herein as a “Gestator”(a Gestator corresponds to the general “gesture engine” above, but theembodiment is not so limited). The Gestator maintains a dynamic list ofall GTots in the active state and a separate such list of all GTots inthe waiting state. These lists are necessarily disjoint. The Gestatorhas a single major execution path: “ProcessTots”.

A fifth principal element of the following gesture engine implementationexamples is the existence of an immediate recipient of the “events”generated through the action of the Gestator. This recipient may be asimple repository, like the FIFO buffer of a dispatch mechanism whosework is to distribute accumulated events periodically to the appropriateend consumers. Alternatively, or additionally, the recipient may be amore complex pipeline that acts to refine, combine, or otherwisecondition the mid-level events provided by the Gestator to producehigher-level event constructs with context-specific information for thebenefit of expectant subsystems.

A single pass through the system's input processing loop, then,comprises allowing the GripeRefinery to update the state of eachGripeEnt; and then executing the Gestator's ProcessTots; which in turn(among other work) entails executing either the EntranceAttempt orContinuationAttempt of every registered GTot.

The Gestator's ProcessTots routine of an embodiment is as follows:

-   -   PT1. Sort the full collection of GTots into meta-sets MS[1 . . .        n]. Sort criteria may be either static or dynamic. A typical        static criterion is “the number of coordinated GripeEnts        required to form the gesture described by a GTot”—the        multiplicity S, above; in such a case, therefore, the meta-set        MS[n] contains those GTots that describe gestures requiring n        GripeEnts.    -   PT2. Select an ordering for the GTots in each meta-set MS [i],        so that MS[i][j] is the jth GTot; the meta-set then comprises        MS[i][1 . . . m]. Sort criteria may again be static or dynamic.        In certain situations the order may correspond simply to the        order in which the GTots were originally instantiated and added        to the Gestator.    -   PT3a. Traverse the Gestator's list of active GTots; execute each        GTot's Update.    -   PT3b. Traverse the Gestator's list of waiting GTots; execute        each GTot's Update.    -   PT4. Construct a list of all GripeEnts. Call it avail_ents.    -   PT5. Traverse the meta-sets from MS[n] to MS[1].    -   PT6a. For each meta-set MS[i], traverse the component GTots from        MS[i][m] to MS[i][1], considering in turn each GTot MS[i][j].    -   PT6b. If MS[i][j] is in the active state, execute its        ContinuationAttempt algorithm, making available to it the list        avail_ents; otherwise, continue with the traversal in (PT6a).    -   PT6c. If the result code from (PT6b) is COPACETIC, continue with        the traversal in (PT6a). The list avail_ents has been modified.    -   PT6d. Or if the result code is EXCLUSIVE, abandon the traversal        in (PT6a) and proceed to (PT7a). The list avail_ents has been        modified.    -   PT6e. Otherwise (the result code is DEMOTE), remove MS[i][j]        from the Gestator's list of active GTots and add it to the list        of waiting GTots; continue with the traversal in (PT6a).    -   PT7a. For each meta-set MS[i], traverse the component GTots from        MS[i][m] to MS[i][1], considering in turn each GTot MS[i][j].    -   PT7b. If MS[i][j] is in the waiting state, execute its        EntranceAttempt algorithm, making available to it the list        avail_ents; otherwise, continue with the traversal in (PT7a).    -   PT7c. If the result code from (PT7b) is COPACETIC, continue with        the traversal in (PT6a).    -   PT7d. The result code is known now to be either PROMOTE or        PROMOTE EXCLUSIVE; remove MS[i][j] from the Gestator's list of        waiting GTots and add it to the list of active GTots. The list        avail_ents has been modified.    -   PT7e. If the result code of (PT7b) is PROMOTE EXCLUSIVE, abandon        the traversal in (PT7a) and proceed to (PT8), concluding the        ProcessTots execution outright.    -   PT7f. Otherwise (the result code is PROMOTE), continue with the        traversal of (PT7a).    -   PT8. Conclude the ProcessTots execution.

A GTot's EntranceAttempt routine of an embodiment is as follows:

-   -   EA1. Traverse the list avail_ents.    -   EA2. Compare elements of avail_ents, taken S at a time (where S        is the multiplicity of the GTot), against the particular        entrance criteria.    -   EA3. If all appropriate combinatorics of the list are exhausted        with no match, return the response code COPACETIC.    -   EA4. Otherwise, some s-tuple of GripeEnts—call it GE[1 . . .        s]—has satisfied the entrance criteria.    -   EA5. Remove each GE[k] of GE[1 . . . S] from the list        avail_ents.    -   EA6. Record, as part of the persistent state carried by the        GTot, each GE[k] of the matching GE[1 . . . S]; these GripeEnts        are now ‘captured’.    -   EA7. Generate and inject into the event queue any such event(s)        as may be appropriate to the description of the GTot's initial        “recognition” of the gesture it describes.    -   EA8. Return the result code PROMOTE or, if the context and        conditioning of the GTot are so disposed, PROMOTE EXCLUSIVE.

A GTot's ContinuationAttempt routine of an embodiment is as follows:

-   -   CA1. Confirm that each of the captured GripeEnts GE[1 . . . S]        is present in the list avail_ents.    -   CA2. If (CA1) is not the case—meaning that a GTot of greater        primacy than the present GTot has usurped one or more of the        component GripeEnts formerly captured by the present GTot—skip        forward to (CA5).    -   CA3. Confirm that each of the captured GripeEnts GE[1 . . . S]        semantically and geometrically satisfies the present GTot's        maintenance criteria.    -   CA4. If (CA3) is the case, skip forward to (CA8); if (CA3) is        not the case—meaning that the gestural inputs comprising GE[1 .        . . S] have “fallen out” of the gesture described by the present        GTot—proceed forward to (CA5).    -   CA5. Generate and inject into the event queue any such event(s)        as may be appropriate to describe the termination of the gesture        described by the present GTot.    -   CA6. Remove references to previously captured GE[1 . . . S] from        the persistent state carried by the present GTot.    -   CA7. Return the result code DEMOTE.    -   CA8. Remove each GE[k] of the captured GripeEnt set GE[1 . . .        S] from the list avail_ents.    -   CA9. Generate, from the presumed-to-have-been-freshly-updated        GE[k], any such event(s) as may be appropriate to the        description of the evolving gesture's state; inject same into        the event queue.    -   CA10. Return the result code COPACETIC or, if the context and        conditioning of the GTot are so disposed, EXCLUSIVE.

Following are detailed descriptions of three exemplary applicationsemploying an embodiment of the spatial-continuum input system introducedabove. Each exemplar supposes that the hands belonging to one or moreoperators are tracked by sensors that resolve the position andorientation of her fingers, and possibly of the overall hand masses, tohigh precision and at a high temporal rate; the system further analyzesthe resulting spatial data in order to characterize the ‘pose’ of eachhand—i.e. the geometric disposition of the fingers relative to eachother and to the hand mass.

The sensors, the approach to tracking and analyzing the operator'shands, and the scheme for representing the hands' poses, positions, andoverall orientations may be as described in detail below; the symbolicpose representations throughout the description following will, for thepurpose of demonstrative specificity, be rendered according to thatnotational scheme. Note however that the embodiments illustrated by theexemplars may make use of other analogous systems, provided that suchare possessed of equivalent symbolic and representational efficacy.

Similarly, the exemplars make reference for the purposes of illustrationto elements of the sample implementation-architecture as outlined above,but alternate, analogous architectures and implementations applyequally.

Where informative, some of the typical higher-level event-structuresthat would be derived during gestural interaction are also reproducedbelow. These events are rendered in a notation consistent with theirpreferred representational scheme, which is as “proteins” built fromprimitive “slawx” (as described in detail below). Such event proteinsare typically deposited in “pools”, a platform-independent,history-retaining interprocess data control and interchange mechanism(also described in detail below); and, after retrieval from theappropriate pools and further context-specific conditioning, aredistributed systematically to programmatic objects that may beinterested. A pool is transparently accessible both to processes localto the machine hosting it and, via the network, to processes executingon remote machines. These event proteins are interspersed throughout theprose descriptions following, and refer in each case to interactionsdescribed in the immediately preceding paragraph. Note too that thearticulated proteins are not necessarily “complete”: certain fields thatmight be necessary to a full running system are elided by way ofpromoting clarity, relevance, and brevity.

A first exemplar is a stereoscopic geometry tutorial. In this firstexemplar, an operator stands next to a horizontal table-like surface,roughly a meter square and at waist height. A display system projectsstereo imagery onto the surface, and additionally tracks the operator'shead so that left- and right-eye views are correctly generated tocorrespond, as she moves about the table, to her instantaneous position.She wears stereo glasses.

The table presents a geometry tutorial; a variety of polyhedra aredimensionally displayed (that is: stereographically, not physically).Each polyhedron is positioned and oriented so that it is stably‘resting’ on one of its faces. Each is about fifteen centimeters high.

The operator raises her right hand and ‘points’ at the table with herindex and middle fingers (the fourth and fifth fingers are curled in,and the thumb points leftward, i.e. perpendicular to the index finger);the hand is oriented so that the palm substantially faces downward. Thispose may be described as [̂̂∥-:v*]. Immediately upon the operator's hand'sadoption of this pose, the table displays a dynamic cursor. Precisegeometric calculations allow the cursor to appear on the surface of thenearest intersected polyhedron (if any) or on the table's surface (if nopolyhedron is intersected). The operator raises her left hand in ananalogous pose and begins pointing with it as well. As a further cue,the polyhedra respond to such ‘pointing interaction’ by turning color:when the operator points with her right hand, the closest intersectedpolyhedron changes from grey to blue; when with her left, from grey togreen. Polyhedra return to their original grey when no longerintersected.

A. [[ DESCRIPS: :event, :pointing, :action, :move, :state, 0, :manus, 3,:evt-grp-qid, 17381 // INGESTS: :gripe => ″{circumflex over( )}{circumflex over ( )}∥-:vx″, :pos => v3 (+215.3|+304.7|+434.6), :aim=> v3(−0.22|−0.51|−0.83) :intersectees => { (:vfeld . 0xaf32b8),(:phys-surf . 0x3dd310), (:geom-obj . 0x4a4e9c) } ]]

While pointing at a dodecahedron the operator articulates her thumb sothat it is briefly brought parallel to and in contact with her indexfinger and then returns to its original, perpendicular orientation([̂̂∥-:-*]-->[̂̂∥|:-*]-->[̂̂∥-:v*]). The dodecahedron reacts by emitting agraphical tag, a square frame that lies dimensionally on the surface ofthe table and that contains the typographic description “{5,3}” (theSchlaefli symbol for the dodecahedron). The tag slides smoothly from itsinitial position near the base of the dodecahedron toward the nearestedge of the table, where it comes to rest. The operator generatesadditional such tags by continuing to point around the table and‘clicking’ her thumb; the resulting tags all end arrayed around the edgeof the table.

B. [[ DESCRIPS: :event, :pointing, :action, :inc-state, :state, 1,:manus, 3, :evt-grp-qid, 17381 // INGESTS: :gripe => ″{circumflex over( )}{circumflex over ( )}|||:vx″, :pos => v3(+124.3|+313.4|+413.2), :aim=> v3 (−0.14|−0.82|−0.55) :intersectees => { (:vfeld . 0xaf32b8),(:phys- surf . 0x3dd310), (:geom-obj . 0x604ddc) } ]] C. [[ DESCRIPS::event, :pointing, :action, :move, :state, 1, :manus, 3, :evt-grp-qid,17381 // INGESTS: :gripe => ″{circumflex over ( )}{circumflex over( )}|||:vx″, :pos => v3 (+124.3|+313.4|+413.2), ... ]] D. [[ DESCRIPS::event, :pointing, :action, :dec-state, :state, 0, :manus, 3,:evt-grp-qid, 17381 // INGESTS: :gripe => ″{circumflex over( )}{circumflex over ( )}|||:vx″, ... ]]

The operator then inclines forward slightly and brings her left hand,palm still facing roughly downward but with middle finger and thumb nowcurled lightly under ([̂̂̂∥>:v*]), downward and over the table surfaceproper. When her hand crosses a threshold-plane twenty centimeters abovethe table, the system's graphical feedback changes: where it formerlyindicated (through the cursor's position) the intersection of theoperator's pointing finger's aim vector with the simulation's varioussurfaces and geometric elements, the feedback system now deploys amultiplicity of “plumb line” cursors that show the closest point onrelative surfaces to the instantaneous position of the finger. So, forexample, a cursor appears directly “under” the finger, tracking alongwith it on the surface of the table. Where the orthogonal projection ofthe finger's position onto the plane of a polyhedral face lies withinthe face's polygon, a cursor appears too. In addition, a faint lateral“horizon line” appears on proximal polyhedral faces in order to suggestthe plane that's parallel to the table and containing the finger'sposition. These graphical marks are updated continuously, at thesystem's natural frame rate (about 90 hertz), and so cognitively “track”the finger's movement.

E. [[ DESCRIPS: :event, :proxing, :action, :move, :manus, 3, :evt-grp-qid, 17385 // INGESTS: :gripe => ″{circumflex over ( )}{circumflex over( )}{circumflex over ( )}|>:vx″, :pos => v3(−93.2|+155.7| +60.8),:proximals => { (:vfeld . 0xaf32b8), (:phys-surf . 0x3dd310), (:geom-obj. 0x4a4e9c), (:geom-obj . 0x604ddc), ... } ]]

When the operator brings her finger to within three centimeters of oneof the polyhedra, the face closest to the approaching finger begins toshift in hue to indicate the near-contact proximity. When at last thefinger geometrically passes through the polyhedron's surface into itsinner volume, the entire form flashes and a synchronized audio cue marksthe occasion of the contact; and, just as when the more distant pointinghand “clicked on” a Platonic form, a graphical tag issues from theform's base and slides to the nearest edge of the table. The operator isthus identically able to access the system's geometric content either bypointing from a distance or by making direct contact.

F. [[ DESCRIPS: :event, :palping, :action, :move, :state, :exterior,:manus , 3, :dactyls, {:index}, :evt-grp-qid, 17391 // INGESTS: :gripe=> ″{circumflex over ( )}{circumflex over ( )}{circumflex over( )}|>:vx″, :pos => v3(...), :palpees => { ((:geom-obj . 0x4a4e9c) .(:dist . +13.5)) } ]] G. [[ DESCRIPS: :event, :palping, :action,:surface- intersect, :state, :interior, :manus, 3, :dactyls, {:index},:evt-grp- qid, 17391 // INGESTS: :gripe => ″{circumflex over( )}{circumflex over ( )}{circumflex over ( )}|>:vx″, :pos => v3 (...),:palpees => { ((:geom-obj . 0x4a4e9c) . (:dist . −1.8)) } ]]

Now the operator extends the middle finger of her active hand so that itis parallel to the index finger ([̂̂∥>:v*]). In this mode, geometriccontact with the system's polyhedra engages simulated physics, so thatwhen the operator pokes at the side of an octahedron it's subject to atorque proportional to the cross product (really the wedge product, fordisciples of geometric algebra) of the poke vector and the radial vectorto the point of contact. In this way, by poking or flicking with herindex and middle fingers, the operator causes the octahedron to rotatearound a vertical (gravity-aligned) axis passing through its bottomface's center. She then stops the rotation by poking her fingersvertically through the topmost volume of the octahedron.

H. [[ DESCRIPS: :event, :palping, :action, :move, :state, :exterior,:manus , 3, :dactyls, {:index, :middle}, :evt-grp-qid, 17394 // INGESTS::gripe => ″{circumflex over ( )}{circumflex over ( )}∥>:vx″, :pos =>v3(...), :ve1 => v3 (...), :palpees => { ((:geom-obj . 0x21b3b8) .(:dist . +21.4)) } ]] I. [[ DESCRIPS: :event, :palping, :action,:surface- intersect, :state, :interior, :manus, 3, :dactyls, {:index,:middle}, :evt-grp-qid, 17394 // INGESTS: :gripe => ″{circumflex over( )}{circumflex over ( )}∥ >:vx″, :pos => v3(...), :ve1 => v3(...),:palpees => { ((:geom-obj . 0x21b3b8) . (:dist . −5.5)) } ]]

Similarly, the operator can reposition the simulation's objects. As shebrings the tips of her index and middle fingers close to the base of atetrahedron, a yellow “underhalo” forms, its brightness in inverserelation to the fingers' proximity to the table surface. When theoperator's fingers come into direct contact (generally the middle fingeris anatomically predisposed to do this first) with the physical surface,the underhalo turns from yellow to red, and a translational offset equalto the fingers' offset from their initial point of contact iscontinuously applied to the tetrahedron. She is thus able to slide anydisplayed object about the table surface; simply breaking contact withthe table ends the sliding interaction.

J. [[ DESCRIPS: :event, :palping, :action, :move, :state, :surface-contact, :manus, 3, :dactyls, {:index, :middle}, :evt-grp-qid, 17394 //INGESTS: :gripe => ″{circumflex over ( )}{circumflex over ( )}∥>:.v″,:pos => v3(...), :ve1 => v3 (...), :palpees => { ((:phys-surf .0x3dd310) . (:dist . −3.0)), ((:geom-obj . 0x9d1104) . (:dist . +19.7))} ]]

Finally, the operator may manipulate the tags that have accumulated atthe table's edges through both distant pointing and proximal pokinginteractions. She brings her left hand down toward the left side of thetable, where a group of tags lies. As any tag is approached by the hand,its luminance begins to grow: in inverse proportion, again, to thedistance from the tag to the nearest finger. In addition, as soon as anyfinger's proximity crosses an outer threshold of five centimeters a line(in the plane of the table surface) snakes its way from the tag to thepolyhedron with which it is associated.

K. [[ DESCRIPS: :event, :palping, :action, :move, :state, :exterior,:manus , 4, :dactyl, :middle, :evt-grp-qid, 17402 // INGESTS: :pos => v3(...), :ve1 => v3(...), :palpees => { ((:phys-surf . 0x3dd310) . (:dist. +9.4)), ((:tag . ″octahedron″) . (:dist . +9.4)) } ]] L. [[ DESCRIPS::event, :palping, :action, :move, :state, :exterior, :manus , 4,:dactyl, :ring, :evt-grp-qid, 17402 // INGESTS: :pos => v3 (...), :ve1=> v3(...), :palpees => { ((:phys-surf . 0x3dd310) . (:dist . +15.8)),((:tag . ″octahedron″) . (:dist . +15.8)), ((:tag . ″tetrahedron″) .(:dist . +15.8)) } ]]

When a finger makes definitive contact with a tag—contact, that is, withthe table surface at a point within the projected tag's geometricbounds—the tag's border turns red; the tag and the finger are logicallybound; and the tag will, so long as the operator's digit remains incontact with the table, follow it, so that the operator is able to slidethe tag about the surface of the table.

M. [[ DESCRIPS: :event, :palping, :action, :move, :state, :surface-contact, :manus, 4, :dactyl, :middle, :evt-grp-qid, 17402 // INGESTS::pos => v3(...), :ve1 => v3(...), :palpees => { ((:phys- surf .0x3dd310) . (:dist . −0.7)), ((:tag . ″octahedron″) . (:dist . −0.7)) }]] N. [[ DESCRIPS: :event, :palping, :action, :move, :state, :exterior,:manus , 4, :dactyl, :ring, :evt-grp-qid, 17402 // INGESTS: :pos => v3(...), :ve1 => v3(...), :palpees => { ((:phys-surf . 0x3dd310) . (:dist. +5.2)), ((:tag . ″tetrahedron″) . (:dist . +5.2)) } ]]

The per-finger binding to individual tags means that, in this mode, theoperator is able to use the fingers of her hand independently: each mayseparately touch and control an individual tag, exactly as if she werelightly touching and sliding a number of coins. As the position of eachsliding tag evolves, the sinuous line between it and its polyhedron isappropriately updated so that the two structures remain graphicallyconnected.

O. [[ DESCRIPS: :event, :palping, :action, :move, :state, :surface-contact, :manus, 4, :dactyl, :middle, :evt-grp-qid, 17402 // INGESTS::pos => v3(...), :ve1 => v3(...), :palpees => { ((:phys- surf .0x3dd310) . (:dist . −0.7)), ((:tag . ″octahedron″) . (:dist . −0.7)) }]] P. [[ DESCRIPS: :event, :palping, :action, :move, :state, :surface-contact, :manus, 4, :dactyl, :ring, :evt-grp-qid, 17402 // INGESTS: :pos=> v3(...), :ve1 => v3(...), :palpees => { ((:phys- surf . 0x3dd310) .(:dist . −1.1)), ((:tag . ″tetrahedron″) . (:dist . −1.1)) } ]]

When the operator raises her finger from a tag it remains in its mostrecent position, so long as that position is still substantially on theedge of the simulation surface; if she releases a tag too far “inland”from the edge, the tag slides radially outward to come to rest onceagain near the edge. Alternately, if she slides any tag off the edge ofthe table, “littering” it onto the floor, that tag is discarded anddisappears.

A second exemplar is a film manipulation system. In this secondexemplar, a ‘rapid-prototyping’ film production workspace comprises twolarge projection screens mounted ninety degrees to each other onintersecting walls and a large, mildly inclined projection tablepositioned two meters from the forward screen. An operator stands justbehind the projection table, facing the forward screen. Displayed on andfilling the forward screen is a still-frame from a sequence of recentlyshot film footage.

Raising his hands to shoulder height, each with palms forward, fingersparallel, and thumbs horizontal ([∥∥-:x̂&&∥∥-:x̂]), the operator gainsaccess to a larger collection of footage sequences (referred to hereinas the “pushback” control system, described in detail below): as hetranslates his hands directly forward, maintaining their overall pose,the current frame recedes as if it were being pushed back intoperspective. As the frame's size on the screen diminishes, it is joinedby other laterally disposed frames, and so the original frame isrevealed to have been just one in a horizontal strip of frames. Theoperator may see more or fewer frames by, respectively, pushing hishands farther forward or pulling them back, the interface modality thusmimicking the physical interaction of shoving against a spring-loadedstructure. Similarly, as the operator moves his hands left and right thehorizontal collection of frames is pulled left and right. The pushbacksystem of an embodiment deliberately constrains navigation by ignoringvertical displacement of the hand, but is not so limited.

A reticle centered graphically on the screen—which appears as soon asthe operator engages the pushback system—indicates which frame will be‘selected’ when pushback interaction is terminated. Moving his hands tothe right, the operator positions a new frame directly under the reticleand closes both hands into fists ([̂̂̂̂>:x && ̂̂̂̂>:x̂]), ending the pushbacksession. The reticle fades as the chosen frame springs forward,centering itself and coming rapidly to fill the projection screen.

The system affords basic control over footage playback: the operator mayplay the footage forward at unit rate by holding either hand in a flatpose parallel to the floor ([∥∥|:vx]) and then rotating it clockwiseuntil the fingers point roughly to the right ([∥∥:vR]). Analogously, thefootage may be played in reverse by rotating the flat handcounterclockwise ([∥∥:vx]-->[∥∥:vL]). He halts playback—“pauses”—byraising either hand in a palm-forward “policeman's halt” pose: [∥∥:x̂].

The operator obtains further control over playback position and rate bybringing either hand into a vertical-plane pose with the fingers pointedforward ([∥∥-x]). So doing engages a “logarithmic timeline”, representedon-screen as a stack of horizontal bars positioned at the bottom of thescreen. The topmost bar in the timeline stack represents the fulltemporal breadth of the footage sequence, the left end of the barcorresponding to the earliest frame and the right end to the finalframe. Each successively lower bar represents a subinterval of thefootage that is smaller (and thus more finely ‘resolved’) by a factor R;the bottommost of the N bars thus represents an interval of footageR̂̂(N−1) smaller than the full duration.

The operator accesses different bars—and thus different temporalresolutions—by translating his hand along a substantially vertical axis(toward or away from the floor). Swinging his hand side to side engagesvariable speed playback of the footage: rightward rotation of the hand(from [∥∥:-x] a little toward [∥∥:x+], if the right hand; or toward[∥∥:.-], if the left) begins to shuttle the footage forward; furtherrotation increases the shuttle speed. Returning the hand to an attitudein which it is pointing directly forward toward the screen bringsplayback to a halt (a small angular detent zone may optionally beprovided about this central angular position to aid reliable maintenanceof halted playback), while continued leftward rotation of the handbegins to shuttle the footage backward. The overall shuttle speed, it isto be understood, is determined by the currently active bar; i.e. thefootage plays, for a given angular attitude of the operator's controlhand, slower by a factor of R̂̂n than it would “on the top bar”, with nthe ordinal of the bar in the range 0 . . . n−1 (zero representing thetop bar).

The lateral extrema of each bar are annotated with the instantaneousfootage timestamp so represented; beneath the stack of bars is displayedthe timestamp belonging to the currently accessed & displayed frame; anda graphical mark depicts, in each bar, the location of that currentframe. Additionally appearing within each bar are graphical indicationsof the temporal loci of “marked frames”—parts of the footage that havein some way been annotated. Some such marks may indicate edit points;the remainder of this exemplar will consider the case in which thesemarks indicate the availability of rotoscoped elements.

When the operator has navigated the footage to a temporal pointcontaining available elements, he releases the control hand from thetimeline pose, and shuttle mode is relinquished. The timeline fades tohalf-transparency, while on the right of the screen a vertical array ofgraphical tags appears; each tag represents an available element withinthe footage's current frame.

The operator brings his hand to head height with fingers extended, thumblateral, and palm parallel to floor ([∥∥-:vx]), and as he does so thetopmost element tag expands and highlights, indicating its selection.Simultaneously, within the paused footage frame on the forward screen,the element so tagged brightens while the rest of the frame dims to alow, fractional luminance. As the operator raises and lowers his hand,keeping the pose roughly constant, he successively accesses the full setof element tags (and each highlights in turn, with the correspondingin-frame indication of the element itself).

With some particular rotoscope element thereby selected and highlighted,the operator uses a pointing gesture (all fingers curled except forindex; thumb aloft; index finger pointing toward the forward screen:[̂̂̂∥-:-x]) followed by a ‘thumb-click’ modification (thumb laid downparallel to index finger: [̂̂̂∥:-x]) to grab the selected element from thefront screen. Immediately, a duplicate of the highlighted elementappears. Where the selected element was static (as part of the pausedfootage's current frame), the new duplicate is “live”, playing forwardand looping at the end of its rotoscope subsequence. The operator then,while maintaining the thumb-clicked pose, lowers his hand until it ispointing at the table's surface; throughout this maneuver, the animatedroto element precisely follows his “aim”, gliding first down the forwardscreen, disappearing into the geometric void between forward screen andtable, and then, when the pointing hand's aim intersects with thetable's quadrilateral surface proper, reappearing there. The operatorcontinues briefly to adjust the location of the animated roto element onthe table by re-aiming his pointing hand; then raises his thumb([̂̂̂∥:-v]-->[̂̂̂|-:-v]), whereupon the element is left in position on thetable.

The operator pulls a second, independent element from the same sequenceframe, repeating the gestural maneuvers above, then shuttle-navigates toanother point in the same sequence to add a third element to thecomposition that is accumulating on the table. He then accesses a whollydifferent sequence (by engaging in pushback interaction) from which headds further elements to the table; and so on.

Eventually the table surface holds a composition containing a dozenrotoscoped elements: isolated characters, props, backgrounds, etc.; theyare all continuously animated, and are drawn in the order in which theywere added to the composition—so that, for opaque elements, the mostrecently added occludes elements lying directly underneath. Because the(full or partial) opacity of these elements and their potentialgeometric overlap mean that ordering is a significant characteristic ofthe composition, it is now appropriate to refer to the elements as“layers”.

Each of the composition's layers is represented by a symbolic, graphicaltag; the collection of tags is arranged along the right edge of thetable's surface (along the forward-back axis). Tags appear as soon asnew elements are added to the table's composition, and the lineararrangement of tags smoothly adjusts to incorporate each newcomer.

The operator now allows his hand to hover about ten centimeters abovethe right side of the table, directly over the tag collection. As hetranslates the hand forward and back, both the tag closest to the handand the element layer to which that tag refers are “selected”: the tagindicates this by sliding further rightward and increasing in brightnessand opacity, while all other composition layers fade to nearly fulltransparency. The selected layer is thus visually isolated).

With his right hand held in a roughly stable position above a particulartag, and with a single layer thereby selected, the operator brings hisleft hand down toward the table surface, somewhere near the visualcenter of the selected layer. As his left hand traverses a firstthreshold plane (parallel to the surface but some twenty centimetersabove it) a visual feedback system is engaged: a regular grid of smallgraphical crosses (“plus symbols”) appears on the surface and ispositionally fixed to it; the brightness and opacity of each cross is afunction both of the height of the hand above the surface and thetwo-dimensional radial distance of the cross from the hand'sdown-projected epicenter. Appropriate scale factors and additiveconstants applied to those two parameters result in the impression of amoving spotlight tracking the hand's position (as projected into contactwith the surface) and illuminating, within a finite-radius circle, afixed grid. The illuminated grid grows brighter as the hand descendstoward the surface.

The operator's left hand, descending, then comes into physical contactwith the table and triggers a change in the feedback system's mode: nowthe feedback grid, formerly fixed to the surface, is dragged along withthe contacting hand. Simultaneously, layer repositioning is engaged, sothat as the operator slides his hand around the table the layer followsprecisely; the impression is one of sliding a piece of paper around on asurface (except of course that the layer itself continues to animate).

Having repositioned the layer to his satisfaction, the operator thenraises his left hand, breaking contact with the surface. The layer‘dragging’ mode is terminated and the layer is left in place. Thegrid-based feedback system simultaneously returns to a previous mode sothat the grid remains fixed with respect to the table surface. Wheneventually the hand moves far enough from the table (vertically), itre-crosses the first threshold and the grid feedback is visuallyextinguished. Moving the right hand sufficiently—either vertically orlaterally (left-right)—similarly exceeds the layer tags' proximitythresholds, and so all tags and layers become unselected, in turnreturning all layers to full opacity so that the entire composition(with one layer now moved) is once more revealed.

The operator engages further layer interaction by bringing his righthand down so that its fingers are fully in contact with one of the layertags near the table's right edge, whereupon the tag brightens and shiftshue to indicate that it has entered a ‘direct manipulation’ mode. Theoperator now slides his hand forward and back along the table, parallelto the right edge; the tag underneath his fingers follows along and, iftranslated far enough, changes its ordinal position (relative to theother tags). When the operator lifts his hand off the surface, the newtag ordering is retained and results in a new logical ordering of thecorresponding layers. In this way the draw-order of the layers may bemanipulated and individual layers may be moved ‘up’ or ‘down’ in thestack.

Finally, a set of ‘instantaneous’ gestures is available to performhigh-level manipulation of the system and its contents. For example, theoperator may sweep his hands, held flat with all fingers and thumbextended and initially pointing uniformly forward, outward and to theside so that the right hand is pointing to the right and the left handis pointing left ([∥∥|:vx & ∥∥|:vx]-->[∥∥:v+ & ∥∥:v+]) in order to clearand delete the composition accumulated so far. Or performing a leftwardtwo-handed ‘pushing’ motion that ends with the hands coplanar with oneheld above the other, palms pointing leftward ([∥∥|:Lx & ∥∥|:Lx]) movesthe forward screen's contents to the left screen: the gesture triggersthe forward footage to begin smoothly translating leftward, rotatingsimultaneously counterclockwise about a vertical axis, until its centerand normal vector coincide with the left screen's center and normal.Entire sections of the application and its component data may thus berearranged or moved between display surfaces.

Note that the film manipulation system described here includes manyfurther facilities—e.g. a mode in which elements can be rotoscoped fromtheir containing sequences literally by hand, the operator's fingertracing out the relevant silhouettes frame by frame; a means for thetime-alignment (or precise time-offset) of elements on the compositiontable; and so on—but the freespace, proximity, and touch-based gesturalinteractions of concern to this disclosure are not further (or uniquely)illuminated through detailed examination of these modes.

A third exemplar is that of a portage slate. In this third exemplar, aninteraction of broad utility involves one or more privileged physicalobjects called “portage slates” (or “p-slates”). Portage slatefunctionality is automatically available to every appropriatelyconfigured application that is built atop the system described herein.The portage slate is a physical tray onto which operators may “scrape”graphical objects and artifacts displayed at a first location, and fromwhich, following peripatetic transport, the enslated objects may betransferred into a new application with a new display context at asecond location.

This third exemplar unfolds with reference to the first and secondexemplars above. The geometry tutorial operator has just “tapped” anicosahedron, so that a tag representing it rests near the edge of thetable, and has subsequently set the icosahedron spinning in the mannerdescribed above. She now picks up, with her left hand, a nearby p-slate.The p-slate is a rigid sheet of matte-colored plastic measuring roughlytwenty by thirty centimeters, and is actively tracked by the system,typically (though not necessarily) through the same mechanisms used totrack the operator's hands.

The operator brings the p-slate close to the table edge and orients itso that the one of the p-slate's edges is parallel to and nearly touchesthe table edge. The projection system that serves the table surface hasbeen arranged so that its expanding cone of pixels deliberately“overfills” the strict physical bounds of the table; in fact, theprojected pixel field is large enough to treat the whole of the p-slatewhen it abuts the table, as it now does.

The proximal presence of the p-slate induces the part of the system withlocal access to the projection pixels to instantiate a transientparasitic display (“TPD”) construct. It is the role of the TPD tomanipulate the subset of pixels that physically land on the p-slate as aseparate display; to compensate for (i.e. invert) any geometricdistortion introduced by the non-normal projective incidence of thepixel subarray on the p-slate; and to provide a virtual renderingcontext to the p-slate and to any processes authorized to draw on it.

It is this rendering context that allows the p-slate to produce ananimated glyph in one of its corners nearest the table as an indicationthat it is live and available. A matching glyph with synchronizedanimation is produced on the table near the p-slate's. The operatorunderstands the glyphic dyad to show that the two surfaces—one fixed,one mobile—are now logically connected.

The operator's right index finger alights on the icosahedron's tag; sheslides her finger—and thus, causally, the tag—around the table'sperimeter and onto the p-slate. The tag, though now outside the table'sboundary, is this time not considered to be discarded (as in theinteraction from the first exemplar, in which tags were “thrown on thefloor”); instead, the system now asserts that the tag is “resident” onthe p-slate.

Indeed, as the operator moves to quit the immediate vicinity of thepolyhedral geometry workstation and set off across the room, hertrajectory brings the p-slate a bit higher and then forward through thetable's projection cone. The system's low-latency tracking enables thechanging position and orientation of the p-slate to update the TPD'sinternal model of the mobile display's geometry; this model allows theTPD construct to continue to commandeer precisely those projector pixelsthat intersect the moving surface. The p-slate simply goes on renderingits contents in place, relative to its own rigid geometry and withoutexplicit reference to (indeed, perhaps computationally oblivious to) thecomplex projective relationship evolving between it and the projector.The operator thus observes that the tag remains in place on the p-slatewhere she'd put it; in every cognitively relevant sense, the tag is nowon the p-slate.

When the operator's forward progress has translated the p-slate so thatits geometry is fully disjunct from the projection pyramid the systemdecommissions the TPD construct and marks its rendering context inert.

As the operator makes her way toward the other end of the long room, shepasses through other volumes in which a projector is active. In eachcase the network-connected tracking system makes its event streamavailable to processes with access to the local projection pixels.Whenever tracking events show that the p-slate is newly intersecting apyramidal projection cone another TPD construct is instantiated. Thep-slate is thus able, during its fleeting passage through the projectionvolume, to render its “contents”; in particular, the icosahedron tag isseen to remain fixed in its position on the p-slate.

Shortly the operator reaches the rapid prototyping film workstation ofthe second exemplar. Here too the projection parameters have beenarranged so that the downward projector's pixels well overfill theassembly table. As the operator brings the p-slate close to theworktable, a local TPD is again instantiated and the tag is revealedonce more. Additionally, the dyad of glyphs again appears—one on thep-slate and one on the table—to indicate that the two surfaces are indata-logical contact.

The operator has positioned the p-slate at the right side of the table'srearmost edge (i.e. nearest her body). She now, holding the p-slate withher left hand, brings her right hand's fingers down onto the tag, whichbrightens to confirm the contact. She smoothly slides the tag forward,across the forward edge of the p-slate, and onto the table. When sheraises her fingers from the table, terminating contact with the tag, itshifts to join the vertical array of rotoscoped elements, which in turnmake slight adjustments to their position to accommodate the new tag.

When now the operator taps lightly on the tag she'd just added to thetable it dispenses its ‘payload’: a two-dimensional projection of anicosahedron, still spinning as she'd left it, slides rapidly from thetag's location, growing in scale as it moves, and takes up a position atthe center of the composition table. The icosahedron now behaves likeany of the other filmic elements used in the composition: the operatormay reposition the icosahedron, change its layer ordering, and so on.

A fourth exemplar involves a general interaction modality that makesparticular use of hover-style input. This fourth exemplar unfolds in thecontext of a city services application; here, a large forward screenshows a three-dimensional view of an urban area, comprising above-groundstructures as well as subterranean features—multiple interpenetratinglayers of electrical, water, gas, sewage, and subway elements. Freespacegestural navigation allows an operator to “fly around” the perspectivescene on the forward display in the manner described in detail below.Meanwhile, a projection table immediately before the operator shows atop-down, orthographic view of the same urban region. The hover-basedinteraction described here allows exploration and selection of theapplication's subsurface structures.

The operator inclines forward slightly and brings his left hand,beginning well above a certain midtown region, down toward the table'ssurface. As the hand descends past a threshold above the table (e.g.,approximately twenty centimeters above the table), the structureselection mechanism is enabled, and a glowing translucent disk isgraphically overlaid on the maplike view of the city. A concise visualrepresentation appears for each subsurface structure that lies withinthe disk's radius. Simultaneously, more detailed three-dimensionaldepictions of those same “highlighted” structures appear at correctthreespace locations on the forward perspective view. As the operatortranslates his hand laterally, holding it at constant height above thesurface, the disk translates accordingly, always appearing directlybeneath the hand. The collection of highlighted structures is updatedcontinuously, so that the moving disk operates as a kind of selectionspotlight.

The interaction subsystem of an embodiment further performs a mapping ofthe tracked hand's height above the table surface and the radius of theselection disk; the mapping is an inverse relationship, so that when theoperator moves his hand closer to the table the disk's size is reduced.The maximum radius is chosen to provide a reasonable “overview”selection as the mode is initially engaged. The minimum radius, achievedwhen the operator's hand is in direct contact with the table surface, ischosen so as to make selection of an individual subsurface element moreor less possible. In this way, the two of a single hand's translationaldegrees of freedom parallel to the display surface are mapped todomain-specific spatial degrees of freedom, while the third axis ismapped to what is notionally a “precision” or “specificity” parameterwithin the application.

Note that in this interaction there is no logical distinction madebetween hover and actual physical table contact (touch)—the system doesnot ascribe differential meaning to touch. However, the operator enjoysa specific physical advantage conferred by the possibility of fullcontact: the selection disk is at its smallest when the operator'sfinger is touching the table, which means that lateral hand motionproduces the largest possible changes in selection (largest, that is,relative to disk size); however, the operator's finger gains substantialpositional stability once in contact with the surface.

The operator may of course also drive the application bimanually, sothat one hand undertakes hover-based element selection and the other isused to perform six degree-of-freedom manipulation of the forwardperspective view. Alternately, two operators may cooperate, each usingone hand.

Finally, the selection may be “locked” by tapping the table in thevicinity of the luminous disk with the second, non-disk-manipulatinghand. This brief tapping activity does, in contrast, distinguish touch.With the selection state thus frozen, the original hand is free to moveaway from the table. In summary, then, the mechanism allows for planarselection of spatially distributed elements with a variable selectionradius.

Naturally, other interaction maneuvers—to condition the application sothat only electrical and gas fixtures are selected, for example—surroundthe core selection activity in an actual deployment. However, theembodiment is not so limited.

Spatial Operating Environment (SOE)

Embodiments of a spatial-continuum input system are described herein inthe context of a Spatial Operating Environment (SOE). As an example,FIG. 10 is a block diagram of a Spatial Operating Environment (SOE),under an embodiment. A user locates his hands 101 and 102 in the viewingarea 150 of an array of cameras 104A-104D. The cameras detect location,orientation, and movement of the fingers and hands 101 and 102, asspatial tracking data, and generate output signals to pre-processor 105.Pre-processor 105 translates the camera output into a gesture signalthat is provided to the computer processing unit 107 of the system. Thecomputer 107 uses the input information to generate a command to controlone or more on screen cursors and provides video output to display 103.

Although the system is shown with a single user's hands as input, theSOE 100 may be implemented using multiple users. In addition, instead ofor in addition to hands, the system may track any part or parts of auser's body, including head, feet, legs, arms, elbows, knees, and thelike.

In the embodiment shown, four cameras or sensors are used to detect thelocation, orientation, and movement of the user's hands 101 and 102 inthe viewing area 150. It should be understood that the SOE 100 mayinclude more (e.g., six cameras, eight cameras, etc.) or fewer (e.g.,two cameras) cameras or sensors without departing from the scope orspirit of the SOE. In addition, although the cameras or sensors aredisposed symmetrically in the example embodiment, there is norequirement of such symmetry in the SOE 100. Any number or positioningof cameras or sensors that permits the location, orientation, andmovement of the user's hands may be used in the SOE 100.

In one embodiment, the cameras used are motion capture cameras capableof capturing grey-scale images. In one embodiment, the cameras used arethose manufactured by Vicon, such as the Vicon MX40 camera. This cameraincludes on-camera processing and is capable of image capture at 1000frames per second. A motion capture camera is capable of detecting andlocating markers.

In the embodiment described, the cameras are sensors used for opticaldetection. In other embodiments, the cameras or other detectors may beused for electromagnetic, magnetostatic, RFID, or any other suitabletype of detection.

Pre-processor 105 generates three dimensional space point reconstructionand skeletal point labeling. The gesture translator 106 converts the 3Dspatial information and marker motion information into a commandlanguage that can be interpreted by a computer processor to update thelocation, shape, and action of a cursor on a display. In an alternateembodiment of the SOE 100, the pre-processor 105 and gesture translator106 are integrated or combined into a single device.

Computer 107 may be any general purpose computer such as manufactured byApple, Dell, or any other suitable manufacturer. The computer 107 runsapplications and provides display output. Cursor information that wouldotherwise come from a mouse or other prior art input device now comesfrom the gesture system.

Marker Tags

The SOE or an embodiment contemplates the use of marker tags on one ormore fingers of the user so that the system can locate the hands of theuser, identify whether it is viewing a left or right hand, and whichfingers are visible. This permits the system to detect the location,orientation, and movement of the user's hands. This information allows anumber of gestures to be recognized by the system and used as commandsby the user.

The marker tags in one embodiment are physical tags comprising asubstrate (appropriate in the present embodiment for affixing to variouslocations on a human hand) and discrete markers arranged on thesubstrate's surface in unique identifying patterns.

The markers and the associated external sensing system may operate inany domain (optical, electromagnetic, magnetostatic, etc.) that allowsthe accurate, precise, and rapid and continuous acquisition of theirthree-space position. The markers themselves may operate either actively(e.g. by emitting structured electromagnetic pulses) or passively (e.g.by being optically retroreflective, as in the present embodiment).

At each frame of acquisition, the detection system receives theaggregate ‘cloud’ of recovered three-space locations comprising allmarkers from tags presently in the instrumented workspace volume (withinthe visible range of the cameras or other detectors). The markers oneach tag are of sufficient multiplicity and are arranged in uniquepatterns such that the detection system can perform the following tasks:(1) segmentation, in which each recovered marker position is assigned toone and only one subcollection of points that form a single tag; (2)labelling, in which each segmented subcollection of points is identifiedas a particular tag; (3) location, in which the three-space position ofthe identified tag is recovered; and (4) orientation, in which thethree-space orientation of the identified tag is recovered. Tasks (1)and (2) are made possible through the specific nature of themarker-patterns, as described below and as illustrated in one embodimentin FIG. 11.

The markers on the tags in one embodiment are affixed at a subset ofregular grid locations. This underlying grid may, as in the presentembodiment, be of the traditional Cartesian sort; or may instead be someother regular plane tessellation (a triangular/hexagonal tilingarrangement, for example). The scale and spacing of the grid isestablished with respect to the known spatial resolution of themarker-sensing system, so that adjacent grid locations are not likely tobe confused. Selection of marker patterns for all tags should satisfythe following constraint: no tag's pattern shall coincide with that ofany other tag's pattern through any combination of rotation,translation, or mirroring. The multiplicity and arrangement of markersmay further be chosen so that loss (or occlusion) of some specifiednumber of component markers is tolerated: After any arbitrarytransformation, it should still be unlikely to confuse the compromisedmodule with any other.

Referring now to FIG. 11, a number of tags 201A-201E (left hand) and202A-202E (right hand) are shown. Each tag is rectangular and consistsin this embodiment of a 5×7 grid array. The rectangular shape is chosenas an aid in determining orientation of the tag and to reduce thelikelihood of mirror duplicates. In the embodiment shown, there are tagsfor each finger on each hand. In some embodiments, it may be adequate touse one, two, three, or four tags per hand. Each tag has a border of adifferent grey-scale or color shade. Within this border is a 3×5 gridarray. Markers (represented by the black dots of FIG. 11) are disposedat certain points in the grid array to provide information.

Qualifying information may be encoded in the tags' marker patternsthrough segmentation of each pattern into ‘common’ and ‘unique’subpatterns. For example, the present embodiment specifies two possible‘border patterns’, distributions of markers about a rectangularboundary. A ‘family’ of tags is thus established—the tags intended forthe left hand might thus all use the same border pattern as shown intags 201A-201E while those attached to the right hand's fingers could beassigned a different pattern as shown in tags 202A-202E. This subpatternis chosen so that in all orientations of the tags, the left pattern canbe distinguished from the right pattern. In the example illustrated, theleft hand pattern includes a marker in each corner and on marker in asecond from corner grid location. The right hand pattern has markers inonly two corners and two markers in non corner grid locations. Aninspection of the pattern reveals that as long as any three of the fourmarkers are visible, the left hand pattern can be positivelydistinguished from the left hand pattern. In one embodiment, the coloror shade of the border can also be used as an indicator of handedness.

Each tag must of course still employ a unique interior pattern, themarkers distributed within its family's common border. In the embodimentshown, it has been found that two markers in the interior grid array aresufficient to uniquely identify each of the ten fingers with noduplication due to rotation or orientation of the fingers. Even if oneof the markers is occluded, the combination of the pattern and thehandedness of the tag yields a unique identifier.

In the present embodiment, the grid locations are visually present onthe rigid substrate as an aid to the (manual) task of affixing eachretroreflective marker at its intended location. These grids and theintended marker locations are literally printed via color inkjet printeronto the substrate, which here is a sheet of (initially) flexible‘shrink-film’. Each module is cut from the sheet and then oven-baked,during which thermal treatment each module undergoes a precise andrepeatable shrinkage. For a brief interval following this procedure, thecooling tag may be shaped slightly—to follow the longitudinal curve of afinger, for example; thereafter, the substrate is suitably rigid, andmarkers may be affixed at the indicated grid points.

In one embodiment, the markers themselves are three dimensional, such assmall reflective spheres affixed to the substrate via adhesive or someother appropriate means. The three-dimensionality of the markers can bean aid in detection and location over two dimensional markers. Howevereither can be used without departing from the spirit and scope of theSOE described herein.

At present, tags are affixed via Velcro or other appropriate means to aglove worn by the operator or are alternately affixed directly to theoperator's fingers using a mild double-stick tape. In a thirdembodiment, it is possible to dispense altogether with the rigidsubstrate and affix—or ‘paint’—individual markers directly onto theoperator's fingers and hands.

Gesture Vocabulary

The SOE of an embodiment contemplates a gesture vocabulary consisting ofhand poses, orientation, hand combinations, and orientation blends. Anotation language is also implemented for designing and communicatingposes and gestures in the gesture vocabulary of the SOE. The gesturevocabulary is a system for representing instantaneous ‘pose states’ ofkinematic linkages in compact textual form. The linkages in question maybe biological (a human hand, for example; or an entire human body; or agrasshopper leg; or the articulated spine of a lemur) or may instead benonbiological (e.g. a robotic arm). In any case, the linkage may besimple (the spine) or branching (the hand). The gesture vocabularysystem of the SOE establishes for any specific linkage a constant lengthstring; the aggregate of the specific ASCII characters occupying thestring's ‘character locations’ is then a unique description of theinstantaneous state, or ‘pose’, of the linkage.

Hand Poses

FIG. 12 illustrates hand poses in an embodiment of a gesture vocabularyof the SOE, under an embodiment. The SOE supposes that each of the fivefingers on a hand is used. These fingers are codes as p-pinkie, r-ringfinger, m-middle finger, i-index finger, and t-thumb. A number of posesfor the fingers and thumbs are defined and illustrated in FIG. 12. Agesture vocabulary string establishes a single character position foreach expressible degree of freedom in the linkage (in this case, afinger). Further, each such degree of freedom is understood to bediscretized (or ‘quantized’), so that its full range of motion can beexpressed through assignment of one of a finite number of standard ASCIIcharacters at that string position. These degrees of freedom areexpressed with respect to a body-specific origin and coordinate system(the back of the hand, the center of the grasshopper's body; the base ofthe robotic arm; etc.). A small number of additional gesture vocabularycharacter positions are therefore used to express the position andorientation of the linkage ‘as a whole’ in the more global coordinatesystem.

Still referring to FIG. 12, a number of poses are defined and identifiedusing ASCII characters. Some of the poses are divided between thumb andnon-thumb. The SOE in this embodiment uses a coding such that the ASCIIcharacter itself is suggestive of the pose. However, any character mayused to represent a pose, whether suggestive or not. In addition, thereis no requirement in the embodiments to use ASCII characters for thenotation strings. Any suitable symbol, numeral, or other representationmaybe used without departing from the scope and spirit of theembodiments. For example, the notation may use two bits per finger ifdesired or some other number of bits as desired.

A curled finger is represented by the character “A” while a curled thumbby “>”. A straight finger or thumb pointing up is indicated by “1” andat an angle by “\” or “/”. “−” represents a thumb pointing straightsideways and “x” represents a thumb pointing into the plane.

Using these individual finger and thumb descriptions, a robust number ofhand poses can be defined and written using the scheme of theembodiments. Each pose is represented by five characters with the orderbeing p-r-m-i-t as described above. FIG. 12 illustrates a number ofposes and a few are described here by way of illustration and example.The hand held flat and parallel to the ground is represented by “11111”.A first is represented by “̂̂̂̂>”. An “OK” sign is represented by “111̂>”.

The character strings provide the opportunity for straightforward ‘humanreadability’ when using suggestive characters. The set of possiblecharacters that describe each degree of freedom may generally be chosenwith an eye to quick recognition and evident analogy. For example, avertical bar (‘|’) would likely mean that a linkage element is‘straight’, an ell (‘L’) might mean a ninety-degree bend, and acircumflex (‘̂’) could indicate a sharp bend. As noted above, anycharacters or coding may be used as desired.

Any system employing gesture vocabulary strings such as described hereinenjoys the benefit of the high computational efficiency of stringcomparison—identification of or search for any specified pose literallybecomes a ‘string compare’ (e.g. UNIX's ‘strcmp( )’ function) betweenthe desired pose string and the instantaneous actual string.Furthermore, the use of ‘wildcard characters’ provides the programmer orsystem designer with additional familiar efficiency and efficacy:degrees of freedom whose instantaneous state is irrelevant for a matchmay be specified as an interrogation point (‘?’); additional wildcardmeanings may be assigned.

Orientation

In addition to the pose of the fingers and thumb, the orientation of thehand can represent information. Characters describing global-spaceorientations can also be chosen transparently: the characters ‘<’, ‘>’,‘̂’, and ‘v’ may be used to indicate, when encountered in an orientationcharacter position, the ideas of left, right, up, and down. FIG. 13illustrates hand orientation descriptors and examples of coding thatcombines pose and orientation. In an embodiment, two character positionsspecify first the direction of the palm and then the direction of thefingers (if they were straight, irrespective of the fingers' actualbends). The possible characters for these two positions express a‘body-centric’ notion of orientation: ‘−’, ‘+’, ‘x’, ‘*’, ‘̂’, and ‘v’describe medial, lateral, anterior (forward, away from body), posterior(backward, away from body), cranial (upward), and caudal (downward).

In the notation scheme of an embodiment, the five finger pose indicatingcharacters are followed by a colon and then two orientation charactersto define a complete command pose. In one embodiment, a start positionis referred to as an “xyz” pose where the thumb is pointing straight up,the index finger is pointing forward and the middle finger isperpendicular to the index finger, pointing to the left when the pose ismade with the right hand. This is represented by the string “̂̂x1-:-x”.

‘XYZ-hand’ is a technique for exploiting the geometry of the human handto allow full six-degree-of-freedom navigation of visually presentedthree-dimensional structure. Although the technique depends only on thebulk translation and rotation of the operator's hand—so that its fingersmay in principal be held in any pose desired—the present embodimentprefers a static configuration in which the index finger points awayfrom the body; the thumb points toward the ceiling; and the middlefinger points left-right. The three fingers thus describe (roughly, butwith clearly evident intent) the three mutually orthogonal axes of athree-space coordinate system: thus ‘XYZ-hand’.

XYZ-hand navigation then proceeds with the hand, fingers in a pose asdescribed above, held before the operator's body at a predetermined‘neutral location’. Access to the three translational and threerotational degrees of freedom of a three-space object (or camera) iseffected in the following natural way: left-right movement of the hand(with respect to the body's natural coordinate system) results inmovement along the computational context's x-axis; up-down movement ofthe hand results in movement along the controlled context's y-axis; andforward-back hand movement (toward/away from the operator's body)results in z-axis motion within the context. Similarly, rotation of theoperator's hand about the index finger leads to a ‘roll’ change of thecomputational context's orientation; ‘pitch’ and ‘yaw’ changes areeffected analogously, through rotation of the operator's hand about themiddle finger and thumb, respectively.

Note that while ‘computational context’ is used here to refer to theentity being controlled by the XYZ-hand method—and seems to suggesteither a synthetic three-space object or camera—it should be understoodthat the technique is equally useful for controlling the various degreesof freedom of real-world objects: the pan/tilt/roll controls of a videoor motion picture camera equipped with appropriate rotational actuators,for example. Further, the physical degrees of freedom afforded by theXYZ-hand posture may be somewhat less literally mapped even in a virtualdomain: In the present embodiment, the XYZ-hand is also used to providenavigational access to large panoramic display images, so thatleft-right and up-down motions of the operator's hand lead to theexpected left-right or up-down ‘panning’ about the image, butforward-back motion of the operator's hand maps to ‘zooming’ control.

In every case, coupling between the motion of the hand and the inducedcomputational translation/rotation may be either direct (i.e. apositional or rotational offset of the operator's hand maps one-to-one,via some linear or nonlinear function, to a positional or rotationaloffset of the object or camera in the computational context) or indirect(i.e. positional or rotational offset of the operator's hand mapsone-to-one, via some linear or nonlinear function, to a first orhigher-degree derivative of position/orientation in the computationalcontext; ongoing integration then effects a non-static change in thecomputational context's actual zero-order position/orientation). Thislatter means of control is analogous to use of a an automobile's ‘gaspedal’, in which a constant offset of the pedal leads, more or less, toa constant vehicle speed.

The ‘neutral location’ that serves as the real-world XYZ-hand's localsix-degree-of-freedom coordinate origin may be established (1) as anabsolute position and orientation in space (relative, say, to theenclosing room); (2) as a fixed position and orientation relative to theoperator herself (e.g. eight inches in front of the body, ten inchesbelow the chin, and laterally in line with the shoulder plane),irrespective of the overall position and ‘heading’ of the operator; or(3) interactively, through deliberate secondary action of the operator(using, for example, a gestural command enacted by the operator's‘other’ hand, said command indicating that the XYZ-hand's presentposition and orientation should henceforth be used as the translationaland rotational origin).

It is further convenient to provide a ‘detent’ region (or ‘dead zone’)about the XYZ-hand's neutral location, such that movements within thisvolume do not map to movements in the controlled context.

Other poses may included:

[∥∥|:vx] is a flat hand (thumb parallel to fingers) with palm facingdown and fingers forward.

[∥∥|:x̂] is a flat hand with palm facing forward and fingers towardceiling.

[∥∥|:-x] is a flat hand with palm facing toward the center of the body(right if left hand, left if right hand) and fingers forward.

[̂̂̂̂-:-x] is a single-hand thumbs-up (with thumb pointing toward ceiling).

[̂̂̂|-:-x] is a mime gun pointing forward.

Two Hand Combination

The SOE of an embodiment contemplates single hand commands and poses, aswell as two-handed commands and poses. FIG. 14 illustrates examples oftwo hand combinations and associated notation in an embodiment of theSOE. Reviewing the notation of the first example, “full stop” revealsthat it comprises two closed fists. The “snapshot” example has the thumband index finger of each hand extended, thumbs pointing toward eachother, defining a goal post shaped frame. The “rudder and throttle startposition” is fingers and thumbs pointing up palms facing the screen.

Orientation Blends

FIG. 15 illustrates an example of an orientation blend in an embodimentof the SOE. In the example shown the blend is represented by enclosingpairs of orientation notations in parentheses after the finger posestring. For example, the first command shows finger positions of allpointing straight. The first pair of orientation commands would resultin the palms being flat toward the display and the second pair has thehands rotating to a 45 degree pitch toward the screen. Although pairs ofblends are shown in this example, any number of blends is contemplatedin the SOE.

Example Commands

FIG. 17A and FIG. 17B illustrate a number of possible commands that maybe used with the SOE. Although some of the discussion here has beenabout controlling a cursor on a display, the SOE is not limited to thatactivity. In fact, the SOE has great application in manipulating any andall data and portions of data on a screen, as well as the state of thedisplay. For example, the commands may be used to take the place ofvideo controls during play back of video media. The commands may be usedto pause, fast forward, rewind, and the like. In addition, commands maybe implemented to zoom in or zoom out of an image, to change theorientation of an image, to pan in any direction, and the like. The SOEmay also be used in lieu of menu commands such as open, close, save, andthe like. In other words, any commands or activity that can be imaginedcan be implemented with hand gestures.

Operation

FIG. 16 is a flow diagram illustrating the operation of the SOE in oneembodiment. At 701 the detection system detects the markers and tags. At702 it is determined if the tags and markers are detected. If not, thesystem returns to 701. If the tags and markers are detected at 702, thesystem proceeds to 703. At 703 the system identifies the hand, fingersand pose from the detected tags and markers. At 704 the systemidentifies the orientation of the pose. At 705 the system identifies thethree dimensional spatial location of the hand or hands that aredetected. (Please note that any or all of 703, 704, and 705 may becombined).

At 706 the information is translated to the gesture notation describedabove. At 707 it is determined if the pose is valid. This may beaccomplished via a simple string comparison using the generated notationstring. If the pose is not valid, the system returns to 701. If the poseis valid, the system sends the notation and position information to thecomputer at 708. At 709 the computer determines the appropriate actionto take in response to the gesture and updates the display accordinglyat 710.

In one embodiment of the SOE, 701-705 are accomplished by the on-cameraprocessor. In other embodiments, the processing can be accomplished bythe system computer if desired.

Parsing and Translation

The system is able to “parse” and “translate” a stream of low-levelgestures recovered by an underlying system, and turn those parsed andtranslated gestures into a stream of command or event data that can beused to control a broad range of computer applications and systems.These techniques and algorithms may be embodied in a system consistingof computer code that provides both an engine implementing thesetechniques and a platform for building computer applications that makeuse of the engine's capabilities.

One embodiment is focused on enabling rich gestural use of human handsin computer interfaces, but is also able to recognize gestures made byother body parts (including, but not limited to arms, torso, legs andthe head), as well as non-hand physical tools of various kinds, bothstatic and articulating, including but not limited to calipers,compasses, flexible curve approximators, and pointing devices of variousshapes. The markers and tags may be applied to items and tools that maybe carried and used by the operator as desired.

The system described here incorporates a number of innovations that makeit possible to build gestural systems that are rich in the range ofgestures that can be recognized and acted upon, while at the same timeproviding for easy integration into applications.

The gestural parsing and translation system in one embodiment comprises:

1) a compact and efficient way to specify (encode for use in computerprograms) gestures at several different levels of aggregation:

-   -   a. a single hand's “pose” (the configuration and orientation of        the parts of the hand relative to one another) a single hand's        orientation and position in three-dimensional space.    -   b. two-handed combinations, for either hand taking into account        pose, position or both.    -   c. multi-person combinations; the system can track more than two        hands, and so more than one person can cooperatively (or        competitively, in the case of game applications) control the        target system.    -   d. sequential gestures in which poses are combined in a series;        we call these “animating” gestures.    -   e. “grapheme” gestures, in which the operator traces shapes in        space.

2) a programmatic technique for registering specific gestures from eachcategory above that are relevant to a given application context.

3) algorithms for parsing the gesture stream so that registered gesturescan be identified and events encapsulating those gestures can bedelivered to relevant application contexts.

The specification system (1), with constituent elements (1a) to (1 f),provides the basis for making use of the gestural parsing andtranslating capabilities of the system described here.

A single-hand “pose” is represented as a string of

i) relative orientations between the fingers and the back of the hand,

ii) quantized into a small number of discrete states.

Using relative joint orientations allows the system described here toavoid problems associated with differing hand sizes and geometries. No“operator calibration” is required with this system. In addition,specifying poses as a string or collection of relative orientationsallows more complex gesture specifications to be easily created bycombining pose representations with further filters and specifications.

Using a small number of discrete states for pose specification makes itpossible to specify poses compactly as well as to ensure accurate poserecognition using a variety of underlying tracking technologies (forexample, passive optical tracking using cameras, active optical trackingusing lighted dots and cameras, electromagnetic field tracking, etc).

Gestures in every category (1a) to (1f) may be partially (or minimally)specified, so that non-critical data is ignored. For example, a gesturein which the position of two fingers is definitive, and other fingerpositions are unimportant, may be represented by a single specificationin which the operative positions of the two relevant fingers is givenand, within the same string, “wild cards” or generic “ignore these”indicators are listed for the other fingers.

All of the innovations described here for gesture recognition, includingbut not limited to the multi-layered specification technique, use ofrelative orientations, quantization of data, and allowance for partialor minimal specification at every level, generalize beyond specificationof hand gestures to specification of gestures using other body parts and“manufactured” tools and objects.

The programmatic techniques for “registering gestures” (2), consist of adefined set of Application Programming Interface calls that allow aprogrammer to define which gestures the engine should make available toother parts of the running system.

These API routines may be used at application set-up time, creating astatic interface definition that is used throughout the lifetime of therunning application. They may also be used during the course of the run,allowing the interface characteristics to change on the fly. Thisreal-time alteration of the interface makes it possible to,

i) build complex contextual and conditional control states,

ii) to dynamically add hysterisis to the control environment, and

iii) to create applications in which the user is able to alter or extendthe interface vocabulary of the running system itself.

Algorithms for parsing the gesture stream (3) compare gestures specifiedas in (1) and registered as in (2) against incoming low-level gesturedata. When a match for a registered gesture is recognized, event datarepresenting the matched gesture is delivered up the stack to runningapplications.

Efficient real-time matching is desired in the design of this system,and specified gestures are treated as a tree of possibilities that areprocessed as quickly as possible.

In addition, the primitive comparison operators used internally torecognize specified gestures are also exposed for the applicationsprogrammer to use, so that further comparison (flexible state inspectionin complex or compound gestures, for example) can happen even fromwithin application contexts.

Recognition “locking” semantics are an innovation of the systemdescribed here. These semantics are implied by the registration API (2)(and, to a lesser extent, embedded within the specification vocabulary(1)). Registration API calls include,

i) “entry” state notifiers and “continuation” state notifiers, and

ii) gesture priority specifiers.

If a gesture has been recognized, its “continuation” conditions takeprecedence over all “entry” conditions for gestures of the same or lowerpriorities. This distinction between entry and continuation states addssignificantly to perceived system usability.

The system described here includes algorithms for robust operation inthe face of real-world data error and uncertainty. Data from low-leveltracking systems may be incomplete (for a variety of reasons, includingocclusion of markers in optical tracking, network drop-out or processinglag, etc).

Missing data is marked by the parsing system, and interpolated intoeither “last known” or “most likely” states, depending on the amount andcontext of the missing data.

If data about a particular gesture component (for example, theorientation of a particular joint) is missing, but the “last known”state of that particular component can be analyzed as physicallypossible, the system uses this last known state in its real-timematching.

Conversely, if the last known state is analyzed as physicallyimpossible, the system falls back to a “best guess range” for thecomponent, and uses this synthetic data in its real-time matching.

The specification and parsing systems described here have been carefullydesigned to support “handedness agnosticism,” so that for multi-handgestures either hand is permitted to satisfy pose requirements.

Navigating Data Space

The SOE of an embodiment enables ‘pushback’, a linear spatial motion ofa human operator's hand, or performance of analogously dimensionalactivity, to control linear verging or trucking motion through agraphical or other data-representational space. The SOE, and thecomputational and cognitive association established by it, provides afundamental, structured way to navigate levels of scale, to traverse aprincipally linear ‘depth dimension’, or—most generally—to accessquantized or ‘detented’ parameter spaces. The SOE also provides aneffective means by which an operator may volitionally acquire additionalcontext: a rapid technique for understanding vicinities andneighborhoods, whether spatial, conceptual, or computational.

In certain embodiments, the pushback technique may employ traditionalinput devices (e.g. mouse, trackball, integrated sliders or knobs) ormay depend on tagged or tracked objects external to the operator's ownperson (e.g. instrumented kinematic linkages, magnetostatically tracked‘input bricks’). In other alternative embodiments, a pushbackimplementation may suffice as the whole of a control system.

The SOE of an embodiment is a component of and integrated into a largerspatial interaction system that supplants customary mouse-basedgraphical user interface (‘WIMP’ UI) methods for control of a computer,comprising instead (a) physical sensors that can track one or more typesof object (e.g., human hands, objects on human hands, inanimate objects,etc.); (b) an analysis component for analyzing the evolving position,orientation, and pose of the sensed hands into a sequence of gesturalevents; (c) a descriptive scheme for representing such spatial andgestural events; (d) a framework for distributing such events to andwithin control programs; (e) methods for synchronizing the human intent(the commands) encoded by the stream of gestural events with graphical,aural, and other display-modal depictions of both the event streamitself and of the application-specific consequences of eventinterpretation, all of which are described in detail below. In such anembodiment, the pushback system is integrated with additional spatialand gestural input-and-interface techniques.

Generally, the navigation of a data space comprises detecting a gestureof a body from gesture data received via a detector. The gesture data isabsolute three-space location data of an instantaneous state of the bodyat a point in time and physical space. The detecting comprisesidentifying the gesture using the gesture data. The navigating comprisestranslating the gesture to a gesture signal, and navigating through thedata space in response to the gesture signal. The data space is adata-representational space comprising a dataset represented in thephysical space.

When an embodiment's overall round-trip latency (hand motion to sensorsto pose analysis to pushback interpretation system to computer graphicsrendering to display device back to operator's visual system) is keptlow (e.g., an embodiment exhibits latency of approximately fifteenmilliseconds) and when other parameters of the system are properlytuned, the perceptual consequence of pushback interaction is a distinctsense of physical causality: the SOE literalizes the physically resonantmetaphor of pushing against a spring-loaded structure. The perceivedcausality is a highly effective feedback; along with other more abstractgraphical feedback modalities provided by the pushback system, and witha deliberate suppression of certain degrees of freedom in theinterpretation of operator movement, such feedback in turn permitsstable, reliable, and repeatable use of both gross and fine human motoractivity as a control mechanism.

In evaluating the context of the SOE, many datasets are inherentlyspatial: they represent phenomena, events, measurements, observations,or structure within a literal physical space. For other datasets thatare more abstract or that encode literal yet non-spatial information, itis often desirable to prepare a representation (visual, aural, orinvolving other display modalities) some fundamental aspect of which iscontrolled by a single, scalar-valued parameter; associating thatparameter with a spatial dimension is then frequently also beneficial.It is manipulation of this single scalar parameter, as is detailedbelow, which benefits from manipulation by means of the pushbackmechanism.

Representations may further privilege a small plurality of discretevalues of their parameter—indeed, sometimes only one—at which thedataset is optimally regarded. In such cases it is useful to speak of a‘detented parameter’ or, if the parameter has been explicitly mappedonto one dimension of a representational space, of ‘detented space’. Useof the term ‘detented’ herein is intended to evoke not only thepreferential quantization of the parameter but also the visuo-hapticsensation of ratchets, magnetic alignment mechanisms, jog-shuttlewheels, and the wealth of other worldly devices that are possessed ofdeliberate mechanical detents.

Self-evident yet crucially important examples of such parameters includebut are not limited to (1) the distance of a synthetic camera, in acomputer graphics environment, from a renderable representation of adataset; (2) the density at which data is sampled from the originaldataset and converted into renderable form; (3) the temporal index atwhich samples are retrieved from a time-varying dataset and converted toa renderable representation. These are universal approaches; countlessdomain-specific parameterizations also exist.

The pushback of the SOE generally aligns the dataset's parameter-controlaxis with a locally relevant ‘depth dimension’ in physical space, andallows structured real-world motion along the depth dimension to effecta data-space translation along the control axis. The result is a highlyefficient means for navigating a parameter space. Following are detaileddescriptions of representative embodiments of the pushback asimplemented in the SOE.

In a pushback example, an operator stands at a comfortable distancebefore a large wall display on which appears a single ‘data frame’comprising text and imagery, which graphical data elements may be staticor dynamic. The data frame, for example, can include an image, but isnot so limited. The data frame, itself a two-dimensional construct, isnonetheless resident in a three-dimensional computer graphics renderingenvironment whose underlying coordinate system has been arranged tocoincide with real-world coordinates convenient for describing the roomand its contents, including the display and the operator.

The operator's hands are tracked by sensors that resolve the positionand orientation of her fingers, and possibly of the overall hand masses,to high precision and at a high temporal rate; the system analyzes theresulting spatial data in order to characterize the ‘pose’ of eachhand—i.e. the geometric disposition of the fingers relative to eachother and to the hand mass. While this example embodiment tracks anobject that is a human hand(s), numerous other objects could be trackedas input devices in alternative embodiments. One example is a one-sidedpushback scenario in which the body is an operator's hand in the openposition, palm facing in a forward direction (along the z-axis) (e.g.,toward a display screen in front of the operator). For the purposes ofthis description, the wall display is taken to occupy the x and ydimensions; z describes the dimension between the operator and thedisplay. The gestural interaction space associated with this pushbackembodiment comprises two spaces abutted at a plane of constant z; thedetented interval space farther from the display (i.e. closer to theoperator) is termed the ‘dead zone’, while the closer half-space is the‘active zone’. The dead zone extends indefinitely in the backwarddirection (toward the operator and away from the display) but only afinite distance forward, ending at the dead zone threshold. The activezone extends from the dead zone threshold forward to the display. Thedata frame(s) rendered on the display are interactively controlled or“pushed back” by movements of the body in the active zone.

The data frame is constructed at a size and aspect ratio preciselymatching those of the display, and is positioned and oriented so thatits center and normal vector coincide with those physical attributes ofthe display, although the embodiment is not so limited. The virtualcamera used to render the scene is located directly forward from thedisplay and at roughly the distance of the operator. In this context,the rendered frame thus precisely fills the display.

Arranged logically to the left and right of the visible frame are anumber of additional coplanar data frames, uniformly spaced and with amodest gap separating each from its immediate neighbors. Because theylie outside the physical/virtual rendering bounds of the computergraphics rendering geometry, these laterally displaced adjacent dataframes are not initially visible. As will be seen, the data space—givenits geometric structure—is possessed of a single natural detent in thez-direction and a plurality of x-detents.

The operator raises her left hand, held in a loose first pose, to hershoulder. She then extends the fingers so that they point upward and thethumb so that it points to the right; her palm faces the screen (in thegestural description language described in detail below, this posetransition would be expressed as [̂̂̂̂>:x̂ into ∥∥-:x̂]). The system,detecting the new pose, triggers pushback interaction and immediatelyrecords the absolute three-space hand position at which the pose wasfirst entered: this position is used as the ‘origin’ from whichsubsequent hand motions will be reported as relative offsets.

Immediately, two concentric, partially transparent glyphs aresuperimposed on the center of the frame (and thus at the display'scenter). For example, the glyphs can indicate body pushback gestures inthe dead zone up to a point of the dead zone threshold. That the secondglyph is smaller than the first glyph is an indication that theoperator's hand resides in the dead zone, through which the pushbackoperation is not ‘yet’ engaged. As the operator moves her hand forward(toward the dead zone threshold and the display), the second glyphincrementally grows. The second glyph is equivalent in size to the firstglyph at the point at which the operator's hand is at the dead zonethreshold. The glyphs of this example describe the evolution of theglyph's concentric elements as the operator's hand travels forward fromits starting position toward the dead zone threshold separating the deadzone from the active zone. The inner “toothy” part of the glyph, forexample, grows as the hand nears the threshold, and is arranged so thatthe radius of the inner glyph and (static) outer glyph precisely matchas the hand reaches the threshold position.

The second glyph shrinks in size inside the first glyph as the operatormoves her hand away from the dead zone threshold and away from thedisplay, remaining however always concentric with the first glyph andcentered on the display. Crucially, only the z-component of theoperator's hand motion is mapped into the glyph's scaling; incidental x-and y-components of the hand motion make no contribution.

When the operator's hand traverses the forward threshold of the deadzone, crossing into the active zone, the pushback mechanism is engaged.The relative z-position of the hand (measured from the threshold) issubjected to a scaling function and the resulting value is used toeffect a z-axis displacement of the data frame and its lateralneighbors, so that the rendered image of the frame is seen to recedefrom the display; the neighboring data frames also then become visible,‘filling in’ from the edges of the display space—the constant angularsubtent of the synthetic camera geometrically ‘captures’ more of theplane in which the frames lie as that plane moves away from the camera.The z-displacement is continuously updated, so that the operator,pushing her hand toward the display and pulling it back toward herself,perceives the lateral collection of frames receding and verging indirect response to her movements

As an example of a first relative z-axis displacement of the data frameresulting from corresponding pushback, the rendered image of the dataframe is seen to recede from the display and the neighboring data framesbecome visible, ‘filling in’ from the edges of the display space. Theneighboring data frames, which include a number of additional coplanardata frames, are arranged logically to the left and right of the visibleframe, uniformly spaced and with a modest gap separating each from itsimmediate neighbors. As an example of a second relative z-axisdisplacement of the data frame resulting from corresponding pushback,and considering the first relative z-axis displacement, and assumingfurther pushing of the operator's hand (pushing further along the z-axistoward the display and away from the operator) from that pushingresulting in the first relative z-axis displacement, the rendered imageof the frame is seen to further recede from the display so thatadditional neighboring data frames become visible, further ‘filling in’from the edges of the display space.

The paired concentric glyphs, meanwhile, now exhibit a modifiedfeedback: with the operator's hand in the active zone, the second glyphswitches from scaling-based reaction to a rotational reaction in whichthe hand's physical z-axis offset from the threshold is mapped into apositive (in-plane) angular offset. In an example of the glyphsindicating body pushback gestures in the dead zone beyond the point ofthe dead zone threshold (along the z-axis toward the display and awayfrom the operator), the glyphs depict the evolution of the glyph oncethe operator's hand has crossed the dead zone threshold—i.e. when thepushback mechanism has been actively engaged. The operator's handmovements toward and away from the display are thus visually indicatedby clockwise and anticlockwise rotation of the second glyph (with thefirst glyph, as before, providing a static reference state), such thatthe “toothy” element of the glyph rotates as a linear function of thehand's offset from the threshold, turning linear motion into arotational representation.

Therefore, in this example, an additional first increment of handmovement along the z-axis toward the display is visually indicated by anincremental clockwise rotation of the second glyph (with the firstglyph, as before, providing a static reference state), such that the“toothy” element of the glyph rotates a first amount corresponding to alinear function of the hand's offset from the threshold. An additionalsecond increment of hand movement along the z-axis toward the display isvisually indicated by an incremental clockwise rotation of the secondglyph (with the first glyph, as before, providing a static referencestate), such that the “toothy” element of the glyph rotates a secondamount corresponding to a linear function of the hand's offset from thethreshold. Further, a third increment of hand movement along the z-axistoward the display is visually indicated by an incremental clockwiserotation of the second glyph (with the first glyph, as before, providinga static reference state), such that the “toothy” element of the glyphrotates a third amount corresponding to a linear function of the hand'soffset from the threshold.

In this sample application, a secondary dimensional sensitivity isengaged when the operator's hand is in the active zone: lateral (x-axis)motion of the hand is mapped, again through a possible scaling function,to x-displacement of the horizontal frame sequence. If the scalingfunction is positive, the effect is one of positional ‘following’ of theoperator's hand, and she perceives that she is sliding the frames leftand right. As an example of a lateral x-axis displacement of the dataframe resulting from lateral motion of the body, the data frames slidefrom left to right such that particular data frames disappear orpartially disappear from view via the left edge of the display spacewhile additional data frames fill in from the right edge of the displayspace.

Finally, when the operator causes her hand to exit the palm-forward pose(by, e.g., closing the hand into a fist), the pushback interaction isterminated and the collection of frames is rapidly returned to itsoriginal z-detent (i.e. coplanar with the display). Simultaneously, theframe collection is laterally adjusted to achieve x-coincidence of asingle frame with the display; which frame ends thus ‘display-centered’is whichever was closest to the concentric glyphs' center at the instantof pushback termination: the nearest x-detent. The glyph structure ishere seen serving a second function, as a selection reticle, but theembodiment is not so limited. The z- and x-positions of the framecollection are typically allowed to progress to their finaldisplay-coincident values over a short time interval in order to providea visual sense of ‘spring-loaded return’.

The pushback system as deployed in this example provides efficientcontrol modalities for (1) acquiring cognitively valuable ‘neighborhoodcontext’ by variably displacing an aggregate dataset along the directvisual sightline—the depth dimension—thereby bringing more of thedataset into view (in exchange for diminishing the angular subtent ofany given part of the dataset); (2) acquiring neighborhood context byvariably displacing the laterally-arrayed dataset along its naturalhorizontal dimension, maintaining the angular subtent of any givensection of data but trading the visibility of old data for that of newdata, in the familiar sense of ‘scrolling’; (3) selecting discretizedelements of the dataset through rapid and dimensionally-constrainednavigation.

In another example of the pushback of an embodiment, an operator standsimmediately next to a waist-level display device whose active surfacelies in a horizontal plane parallel to the floor. The coordinate systemis here established in a way consistent with that of the previousexample: the display surface lies in the x-z plane, so that the y-axis,representing the normal to the surface, is aligned in opposition to thephysical gravity vector.

In an example physical scenario in which the body is held horizontallyabove a table-like display surface, the body is an operator's hand, butthe embodiment is not so limited. The pushback interaction isdouble-sided, so that there is an upper dead zone threshold and a lowerdead zone threshold. Additionally, the linear space accessed by thepushback maneuver is provided with discrete spatial detents (e.g.,“1^(st) detent”, “2^(nd) detent”, “3^(rd) detent”, “4^(th) detent”) inthe upper active zone, and discrete spatial detents (e.g., “1^(st)detent”, “2^(nd) detent”, “3^(rd) detent”, “4^(th) detent”) in the loweractive zone. The interaction space of an embodiment is configured sothat a relatively small dead zone comprising an upper dead zone and alower dead zone is centered at the vertical (y-axis) position at whichpushback is engaged, with an active zone above the dead zone and anactive zone below the dead zone.

The operator is working with an example dataset that has been analyzedinto a stack of discrete parallel planes that are the data frames. Thedataset may be arranged that way as a natural consequence of thephysical reality it represents (e.g. discrete slices from a tomographicscan, the multiple layers of a three-dimensional integrated circuit,etc.) or because it is logical or informative to separate and discretizethe data (e.g., satellite imagery acquired in a number of spectralbands, geographically organized census data with each decade's data in aseparate layer, etc.). The visual representation of the data may furtherbe static or include dynamic elements.

During intervals when pushback functionality is not engaged, a singlelayer is considered ‘current’ and is represented with visual prominenceby the display, and is perceived to be physically coincident with thedisplay. Layers above and below the current layer are in this examplenot visually manifest (although a compact iconography is used toindicate their presence).

The operator extends his closed right hand over the display; when heopens the hand—fingers extended forward, thumb to the left, and palmpointed downward (transition: [̂̂̂̂>:vx into ∥∥-:vx])—the pushback system isengaged. During a brief interval (e.g., 200 milliseconds), some numberof layers adjacent to the current layer fade up with differentialvisibility; each is composited below or above with a blur filter and atransparency whose ‘severities’ are dependent on the layer's ordinaldistance from the current layer.

For example, a layer (e.g., data frame) adjacent to the current layer(e.g., data frame) fades up with differential visibility as the pushbacksystem is engaged. In this example, the stack comprises numerous dataframes (any number as appropriate to datasets of the data frames) thatcan be traversed using the pushback system.

Simultaneously, the concentric feedback glyphs familiar from theprevious example appear; in this case, the interaction is configured sothat a small dead zone is centered at the vertical (y-axis) position atwhich pushback is engaged, with an active zone both above and below thedead zone. This arrangement provides assistance in ‘regaining’ theoriginal layer. The glyphs are in this case accompanied by anadditional, simple graphic that indicates directed proximity tosuccessive layers.

While the operator's hand remains in the dead zone, no displacement ofthe layer stack occurs. The glyphs exhibit a ‘preparatory’ behavioridentical to that in the preceding example, with the inner glyph growingas the hand nears either boundary of the zone (of course, here thebehavior is double-sided and symmetric: the inner glyph is at a minimumscale at the hand's starting y-position and grows toward coincidencewith the outer glyph whether the hand moves up or down).

As the operator's hand moves upward past the dead zone's upper plane,the inner glyph engages the outer glyph and, as before, further movementof the hand in that direction causes anticlockwise rotational motion ofthe inner glyph. At the same time, the layer stack begins to ‘translateupward’: those layers above the originally-current layer take on greatertransparency and blur; the originally-current layer itself becomes moretransparent and more blurred; and the layers below it move toward morevisibility and less blur.

In another example of upward translation of the stack, thepreviously-current layer takes on greater transparency (becomesinvisible in this example), while the layer adjacent to thepreviously-current layer becomes visible as the presently-current layer.Additionally, layer adjacent to the presently-current layer fades upwith differential visibility as the stack translates upward. Asdescribed above, the stack comprises numerous data frames (any number asappropriate to datasets of the data frames) that can be traversed usingthe pushback system.

The layer stack is configured with a mapping between real-worlddistances (i.e. the displacement of the operator's hand from its initialposition, as measured in room coordinates) and the ‘logical’ distancebetween successive layers. The translation of the layer stack is, ofcourse, the result of this mapping, as is the instantaneous appearanceof the proximity graphic, which meanwhile indicates (at first) a growingdistance between the display plane and the current layer; it alsoindicates that the display plane is at present below the current layer.

The hand's motion continues and the layer stack eventually passes theposition at which the current layer and the next one below exactlystraddle (i.e. are equidistant from) the display plane; just past thispoint the proximity graphic changes to indicate that the display planeis now higher than the current layer: ‘current layer status’ has nowbeen assigned to the next lower layer. In general, the current layer isalways the one closest to the physical display plane, and is the onethat will be ‘selected’ when the operator disengages the pushbacksystem.

As the operator continues to raise his hand, each consecutive layer isbrought toward the display plane, becoming progressively more resolved,gaining momentary coincidence with the display plane, and then returningtoward transparency and blur in favor of the next lower layer. When theoperator reverses the direction of his hand's motion, lowering it, theprocess is reversed, and the inner glyph rotates clockwise. As the handeventually passes through the dead zone the stack halts with theoriginally-current layer in precise y-alignment with the display plane;and then y-travel of the stack resumes, bringing into successive focusthose planes above the originally-current layer. The operator's overallperception is strongly and simply that he is using his hand to push downand pull up a stack of layers.

When at last the operator releases pushback by closing his hand (orotherwise changing its pose) the system ‘springs’ the stack intodetented y-axis alignment with the display plane, leaving as the currentlayer whichever was closest to the display plane as pushback was exited.During the brief interval of this positional realignment, all otherlayers fade back to complete transparency and the feedback glyphssmoothly vanish.

The discretized elements of the dataset (here, layers) of this exampleare distributed along the principal pushback (depth) axis; previously,the elements (data frames) were coplanar and arrayed laterally, along adimension orthogonal to the depth axis. This present arrangement, alongwith the deployment of transparency techniques, means that data is oftensuperimposed—some layers are viewed through others. The operator in thisexample nevertheless also enjoys (1) a facility for rapidly gainingneighborhood context (what are the contents of the layers above andbelow the current layer?); and (2) a facility for efficiently selectingand switching among parallel, stacked elements in the dataset. When theoperator intends (1) alone, the provision of a dead zone allows him toreturn confidently to the originally selected layer. Throughout themanipulation, the suppression of two translational dimensions enablesspeed and accuracy (it is comparatively difficult for most humans totranslate a hand vertically with no lateral drift, but the modality asdescribed simply ignores any such lateral displacement).

It is noted that for certain purposes it may be convenient to configurethe pushback input space so that the dead zone is of infinitesimalextent; then, as soon as pushback is engaged, its active mechanisms arealso engaged. In the second example presented herein this would meanthat the originally—current layer is treated no differently—once thepushback maneuver has begun—from any other. Empirically, the linearextent of the dead zone is a matter of operator preference.

The modalities described in this second example are pertinent across awide variety of displays, including both two-dimensional (whetherprojected or emissive) and three-dimensional (whether autostereoscopicor not, aerial-image-producing or not, etc.) devices. In high-qualityimplementations of the latter—i.e. 3D—case, certain characteristics ofthe medium can vastly aid the perceptual mechanisms that underliepushback. For example, a combination of parallax, optical depth offield, and ocular accommodation phenomena can allow multiple layers tobe apprehended simultaneously, thus eliminating the need to severelyfade and blur (or indeed to exclude altogether) layers distant from thedisplay plane. The modalities apply, further, irrespective of theorientation of the display: it may be principally horizontal, as in theexample, or may just as usefully be mounted at eye-height on a wall.

An extension to the scenario of this second example depicts theusefulness of two-handed manipulation. In certain applications,translating either the entire layer stack or an individual layerlaterally (i.e. in the x and z directions) is necessary. In anembodiment, the operator's other—that is, non-pushback—hand can effectthis transformation, for example through a modality in which bringingthe hand into close proximity to the display surface allows one of thedataset's layers to be ‘slid around’, so that its offset x-z positionfollows that of the hand.

Operators may generally find it convenient and easily tractable toundertake lateral translation and pushback manipulations simultaneously.It is perhaps not wholly fatuous to propose that the assignment ofcontinuous-domain manipulations to one hand and discrete-style work tothe other may act to optimize cognitive load.

It is informative to consider yet another example of pushback under theSOE in which there is no natural visual aspect to the dataset.Representative is the problem of monitoring a plurality of audiochannels and of intermittently selecting one from among the collection.An application of the pushback system enables such a task in anenvironment outfitted for aural but not visual output; the modality isremarkably similar to that of the preceding example.

An operator, standing or seated, is listening to a single channel ofaudio. Conceptually, this audio exists in the vertical plane—called the‘aural plane’—that geometrically includes her ears; additional channelsof audio are resident in additional planes parallel to the aural planebut displaced forward and back, along the z-axis.

Opening her hand, held nine inches in front of her, with palm facingforward, she engages the pushback system. The audio in several proximalplanes fades up differentially; the volume of each depends inversely onits ordinal distance from the current channel's plane. In practice, itis perceptually unrealistic to allow more than two or four additionalchannels to become audible. At the same time, an ‘audio glyph’ fades upto provide proximity feedback. Initially, while the operator's hand isheld in the dead zone, the glyph is a barely audible two-note chord(initially in unison).

As the operator moves her hand forward or backward through the deadzone, the volumes of the audio channels remain fixed while that of theglyph increases. When the hand crosses the front or rear threshold ofthe dead zone, the glyph reaches its ‘active’ volume (which is stillsubordinate to the current channel's volume).

Once the operator's hand begins moving through the active zone—in theforward direction, say—the expected effect on the audio channelsobtains: the current channel plane is pushed farther from the auralplane, and its volume (and the volumes of those channels still fartherforward) is progressively reduced. The volume of each ‘dorsal’ channelplane, on the other hand, increases as it nears the aural plane.

The audio glyph, meanwhile, has switched modes. The hand's forwardprogress is accompanied by the rise in frequency of one of the tones; atthe ‘midway point’, when the aural plane bisects one audio channel planeand the next, the tones form an exact fifth (mathematically, it shouldbe a tritone interval, but there is an abundance of reasons that this isto be eschewed). The variable tone's frequency continues rising as thehand continues farther forward, until eventually the operator ‘reaches’the next audio plane, at which point the tones span precisely an octave.

Audition of the various channels proceeds, the operator translating herhand forward and back to access each in turn Finally, to select one shemerely closes her hand, concluding the pushback session and causing thecollection of audio planes to ‘spring’ into alignment. The other(non-selected) channels fade to inaudibility, as does the glyph.

This example has illustrated a variant on pushback application in whichthe same facilities are again afforded: access to neighborhood contextand rapid selection of discretized data element (here, an individualaudio stream). The scenario substitutes an aural feedback mechanism, andin particular one that exploits the reliable human capacity fordiscerning certain frequency intervals, to provide the operator withinformation about whether she is ‘close enough’ to a target channel tomake a selection. This is particularly important in the case of voicechannels, in which ‘audible’ signals are only intermittently present;the continuous nature of the audio feedback glyph leaves it present andlegible even when the channel itself has gone silent.

It is noted that if the SOE in this present example includes thecapacity for spatialized audio, the perception of successive audiolayers receding into the forward distance and approaching from the back(or vice versa) may be greatly enhanced. Further, the opportunity tomore literally ‘locate’ the selected audio plane at the position of theoperator, with succeeding layers in front of the operator and precedinglayers behind, is usefully exploitable.

Other instantiations of the audio glyph are possible, and indeed thenature of the various channels' contents, including their spectraldistributions, tends to dictate which kind of glyph will be most clearlydiscernible. By way of example, another audio glyph format maintainsconstant volume but employs periodic clicking, with the interval betweenclicks proportional to the proximity between the aural plane and theclosest audio channel plane. Finally, under certain circumstances, anddepending on the acuity of the operator, it is possible to use audiopushback with no feedback glyph at all.

With reference to the pushback mechanism, as the number and density ofspatial detents in the dataset's representation increases toward thevery large, the space and its parameterization becomes effectivelycontinuous—that is to say, non-detented. Pushback remains nonethelesseffective at such extremes, in part because the dataset's ‘initialstate’ prior to each invocation of pushback may be treated as atemporary detent, realized simply as a dead zone.

An application of such non-detented pushback may be found in connectionwith the idea of an infinitely (or at least substantially) zoomablediagram. Pushback control of zoom functionality associates offset handposition with affine scale value, so that as the operator pushes hishand forward or back the degree of zoom decreases or increases(respectively). The original, pre-pushback zoom state is always readilyaccessible, however, because the direct mapping of position to zoomparameter insures that returning the control hand to the dead zone alsoeffects return of the zoom value to its initial state.

Each scenario described in the examples above provides a description ofthe salient aspects of the pushback system and its use under the SOE. Itshould further be understood that each of the maneuvers described hereincan be accurately and comprehensibly undertaken in a second or less,because of the efficiency and precision enabled by allowing a particularkind of perceptual feedback to guide human movement. At other times,operators also find it useful to remain in a single continuous pushback‘session’ for tens of seconds: exploratory and context-acquisition goalsare well served by pushback over longer intervals.

The examples described above employed a linear mapping of physical input(gesture) space to representational space: translating the control handby A units in real space always results in a translation by B units[prime] in the representational space, irrespective of the real-spaceposition at which the A-translation is undertaken. However, othermappings are possible. In particular, the degree of fine motor controlenjoyed by most human operators allows the use of nonlinear mappings, inwhich for example differential gestural translations far from the activethreshold can translate into larger displacements along theparameterized dimension than do gestural translations near thethreshold.

Coincident Virtual/Display and Physical Spaces

The system can provide an environment in which virtual space depicted onone or more display devices (“screens”) is treated as coincident withthe physical space inhabited by the operator or operators of the system.An embodiment of such an environment is described here. This currentembodiment includes three projector-driven screens at fixed locations,is driven by a single desktop computer, and is controlled using thegestural vocabulary and interface system described herein. Note,however, that any number of screens are supported by the techniquesbeing described; that those screens may be mobile (rather than fixed);that the screens may be driven by many independent computerssimultaneously; and that the overall system can be controlled by anyinput device or technique.

The interface system described in this disclosure should have a means ofdetermining the dimensions, orientations and positions of screens inphysical space. Given this information, the system is able todynamically map the physical space in which these screens are located(and which the operators of the system inhabit) as a projection into thevirtual space of computer applications running on the system. As part ofthis automatic mapping, the system also translates the scale, angles,depth, dimensions and other spatial characteristics of the two spaces ina variety of ways, according to the needs of the applications that arehosted by the system.

This continuous translation between physical and virtual space makespossible the consistent and pervasive use of a number of interfacetechniques that are difficult to achieve on existing applicationplatforms or that must be implemented piece-meal for each applicationrunning on existing platforms. These techniques include (but are notlimited to):

1) Use of “literal pointing”—using the hands in a gestural interfaceenvironment, or using physical pointing tools or devices—as a pervasiveand natural interface technique.

2) Automatic compensation for movement or repositioning of screens.

3) Graphics rendering that changes depending on operator position, forexample simulating parallax shifts to enhance depth perception.

4) Inclusion of physical objects in on-screen display—taking intoaccount real-world position, orientation, state, etc. For example, anoperator standing in front of a large, opaque screen, could see bothapplications graphics and a representation of the true position of ascale model that is behind the screen (and is, perhaps, moving orchanging orientation).

It is important to note that literal pointing is different from theabstract pointing used in mouse-based windowing interfaces and mostother contemporary systems. In those systems, the operator must learn tomanage a translation between a virtual pointer and a physical pointingdevice, and must map between the two cognitively.

By contrast, in the systems described in this disclosure, there is nodifference between virtual and physical space (except that virtual spaceis more amenable to mathematical manipulation), either from anapplication or user perspective, so there is no cognitive translationrequired of the operator.

The closest analogy for the literal pointing provided by the embodimentdescribed here is the touch-sensitive screen (as found, for example, onmany ATM machines). A touch-sensitive screen provides a one to onemapping between the two-dimensional display space on the screen and thetwo-dimensional input space of the screen surface. In an analogousfashion, the systems described here provide a flexible mapping(possibly, but not necessarily, one to one) between a virtual spacedisplayed on one or more screens and the physical space inhabited by theoperator. Despite the usefulness of the analogy, it is worthunderstanding that the extension of this “mapping approach” to threedimensions, an arbitrarily large architectural environment, and multiplescreens is non-trivial.

In addition to the components described herein, the system may alsoimplement algorithms implementing a continuous, systems-level mapping(perhaps modified by rotation, translation, scaling or other geometricaltransformations) between the physical space of the environment and thedisplay space on each screen.

A rendering stack which takes the computational objects and the mappingand outputs a graphical representation of the virtual space.

An input events processing stack which takes event data from a controlsystem (in the current embodiment both gestural and pointing data fromthe system and mouse input) and maps spatial data from input events tocoordinates in virtual space. Translated events are then delivered torunning applications.

A “glue layer” allowing the system to host applications running acrossseveral computers on a local area network.

Embodiments of a spatial-continuum input system are described herein ascomprising network-based data representation, transit, and interchangethat includes a system called “plasma” that comprises subsystems“slawx”, “proteins”, and “pools”, as described in detail below. Thepools and proteins are components of methods and systems describedherein for encapsulating data that is to be shared between or acrossprocesses. These mechanisms also include slawx (plural of “slaw”) inaddition to the proteins and pools. Generally, slawx provide thelowest-level of data definition for inter-process exchange, proteinsprovide mid-level structure and hooks for querying and filtering, andpools provide for high-level organization and access semantics. Slawxinclude a mechanism for efficient, platform-independent datarepresentation and access. Proteins provide a data encapsulation andtransport scheme using slawx as the payload. Pools provide structuredand flexible aggregation, ordering, filtering, and distribution ofproteins within a process, among local processes, across a networkbetween remote or distributed processes, and via longer term (e.g.on-disk, etc.) storage.

The configuration and implementation of the embodiments described hereininclude several constructs that together enable numerous capabilities.For example, the embodiments described herein provide efficient exchangeof data between large numbers of processes as described above. Theembodiments described herein also provide flexible data “typing” andstructure, so that widely varying kinds and uses of data are supported.Furthermore, embodiments described herein include flexible mechanismsfor data exchange (e.g., local memory, disk, network, etc.), all drivenby substantially similar application programming interfaces (APIs).Moreover, embodiments described enable data exchange between processeswritten in different programming languages. Additionally, embodimentsdescribed herein enable automatic maintenance of data caching andaggregate state.

FIG. 18 is a block diagram of a processing environment including datarepresentations using slawx, proteins, and pools, under an embodiment.The principal constructs of the embodiments presented herein includeslawx (plural of “slaw”), proteins, and pools. Slawx as described hereinincludes a mechanism for efficient, platform-independent datarepresentation and access. Proteins, as described in detail herein,provide a data encapsulation and transport scheme, and the payload of aprotein of an embodiment includes slawx. Pools, as described herein,provide structured yet flexible aggregation, ordering, filtering, anddistribution of proteins. The pools provide access to data, by virtue ofproteins, within a process, among local processes, across a networkbetween remote or distributed processes, and via ‘longer term’ (e.g.on-disk) storage.

FIG. 19 is a block diagram of a protein, under an embodiment. Theprotein includes a length header, a descrip, and an ingest. Each of thedescrip and ingest includes slaw or slawx, as described in detail below.

FIG. 20 is a block diagram of a descrip, under an embodiment. Thedescrip includes an offset, a length, and slawx, as described in detailbelow.

FIG. 21 is a block diagram of an ingest, under an embodiment. The ingestincludes an offset, a length, and slawx, as described in detail below.

FIG. 22 is a block diagram of a slaw, under an embodiment. The slawincludes a type header and type-specific data, as described in detailbelow.

FIG. 23A is a block diagram of a protein in a pool, under an embodiment.The protein includes a length header (“protein length”), a descripsoffset, an ingests offset, a descrip, and an ingest. The descripsincludes an offset, a length, and a slaw. The ingest includes an offset,a length, and a slaw.

The protein as described herein is a mechanism for encapsulating datathat needs to be shared between processes, or moved across a bus ornetwork or other processing structure. As an example, proteins providean improved mechanism for transport and manipulation of data includingdata corresponding to or associated with user interface events; inparticular, the user interface events of an embodiment include those ofthe gestural interface described above. As a further example, proteinsprovide an improved mechanism for transport and manipulation of dataincluding, but not limited to, graphics data or events, and stateinformation, to name a few. A protein is a structured record format andan associated set of methods for manipulating records. Manipulation ofrecords as used herein includes putting data into a structure, takingdata out of a structure, and querying the format and existence of data.Proteins are configured to be used via code written in a variety ofcomputer languages. Proteins are also configured to be the basicbuilding block for pools, as described herein. Furthermore, proteins areconfigured to be natively able to move between processors and acrossnetworks while maintaining intact the data they include.

In contrast to conventional data transport mechanisms, proteins areuntyped. While being untyped, the proteins provide a powerful andflexible pattern-matching facility, on top of which “type-like”functionality is implemented. Proteins configured as described hereinare also inherently multi-point (although point-to-point forms areeasily implemented as a subset of multi-point transmission).Additionally, proteins define a “universal” record format that does notdiffer (or differs only in the types of optional optimizations that areperformed) between in-memory, on-disk, and on-the-wire (network)formats, for example.

Referring to FIGS. 19 and 23A, a protein of an embodiment is a linearsequence of bytes. Within these bytes are encapsulated a descrips listand a set of key-value pairs called ingests. The descrips list includesan arbitrarily elaborate but efficiently filterable per-protein eventdescription. The ingests include a set of key-value pairs that comprisethe actual contents of the protein.

Proteins' concern with key-value pairs, as well as some core ideas aboutnetwork-friendly and multi-point data interchange, is shared withearlier systems that privilege the concept of “tuples” (e.g., Linda,Jini). Proteins differ from tuple-oriented systems in several majorways, including the use of the descrips list to provide a standard,optimizable pattern matching substrate. Proteins also differ fromtuple-oriented systems in the rigorous specification of a record formatappropriate for a variety of storage and language constructs, along withseveral particular implementations of “interfaces” to that recordformat.

Turning to a description of proteins, the first four or eight bytes of aprotein specify the protein's length, which must be a multiple of 16bytes in an embodiment. This 16-byte granularity ensures thatbyte-alignment and bus-alignment efficiencies are achievable oncontemporary hardware. A protein that is not naturally “quad-wordaligned” is padded with arbitrary bytes so that its length is a multipleof 16 bytes.

The length portion of a protein has the following format: 32 bitsspecifying length, in big-endian format, with the four lowest-order bitsserving as flags to indicate macro-level protein structurecharacteristics; followed by 32 further bits if the protein's length isgreater than 2̂32 bytes.

The 16-byte-alignment proviso of an embodiment means that the lowestorder bits of the first four bytes are available as flags. And so thefirst three low-order bit flags indicate whether the protein's lengthcan be expressed in the first four bytes or requires eight, whether theprotein uses big-endian or little-endian byte ordering, and whether theprotein employs standard or non-standard structure, respectively, butthe protein is not so limited. The fourth flag bit is reserved forfuture use.

If the eight-byte length flag bit is set, the length of the protein iscalculated by reading the next four bytes and using them as thehigh-order bytes of a big-endian, eight-byte integer (with the fourbytes already read supplying the low-order portion). If thelittle-endian flag is set, all binary numerical data in the protein isto be interpreted as little-endian (otherwise, big-endian). If thenon-standard flag bit is set, the remainder of the protein does notconform to the standard structure to be described below.

Non-standard protein structures will not be discussed further herein,except to say that there are various methods for describing andsynchronizing on non-standard protein formats available to a systemsprogrammer using proteins and pools, and that these methods can beuseful when space or compute cycles are constrained. For example, theshortest protein of an embodiment is sixteen bytes. A standard-formatprotein cannot fit any actual payload data into those sixteen bytes (thelion's share of which is already relegated to describing the location ofthe protein's component parts). But a non-standard format protein couldconceivably use 12 of its 16 bytes for data. Two applications exchangingproteins could mutually decide that any 16-byte-long proteins that theyemit always include 12 bytes representing, for example, 12 8-bit sensorvalues from a real-time analog-to-digital converter.

Immediately following the length header, in the standard structure of aprotein, two more variable-length integer numbers appear. These numbersspecify offsets to, respectively, the first element in the descrips listand the first key-value pair (ingest). These offsets are also referredto herein as the descrips offset and the ingests offset, respectively.The byte order of each quad of these numbers is specified by the proteinendianness flag bit. For each, the most significant bit of the firstfour bytes determines whether the number is four or eight bytes wide. Ifthe most significant bit (msb) is set, the first four bytes are the mostsignificant bytes of a double-word (eight byte) number. This is referredto herein as “offset form”. Use of separate offsets pointing to descripsand pairs allows descrips and pairs to be handled by different codepaths, making possible particular optimizations relating to, forexample, descrips pattern-matching and protein assembly. The presence ofthese two offsets at the beginning of a protein also allows for severaluseful optimizations.

Most proteins will not be so large as to require eight-byte lengths orpointers, so in general the length (with flags) and two offset numberswill occupy only the first three bytes of a protein. On many hardware orsystem architectures, a fetch or read of a certain number of bytesbeyond the first is “free” (e.g., 16 bytes take exactly the same numberof clock cycles to pull across the Cell processor's main bus as a singlebyte).

In many instances it is useful to allow implementation-specific orcontext-specific caching or metadata inside a protein. The use ofoffsets allows for a “hole” of arbitrary size to be created near thebeginning of the protein, into which such metadata may be slotted. Animplementation that can make use of eight bytes of metadata gets thosebytes for free on many system architectures with every fetch of thelength header for a protein.

The descrips offset specifies the number of bytes between the beginningof the protein and the first descrip entry. Each descrip entry comprisesan offset (in offset form, of course) to the next descrip entry,followed by a variable-width length field (again in offset format),followed by a slaw. If there are no further descrips, the offset is, byrule, four bytes of zeros. Otherwise, the offset specifies the number ofbytes between the beginning of this descrip entry and a subsequentdescrip entry. The length field specifies the length of the slaw, inbytes.

In most proteins, each descrip is a string, formatted in the slaw stringfashion: a four-byte length/type header with the most significant bitset and only the lower 30 bits used to specify length, followed by theheader's indicated number of data bytes. As usual, the length headertakes its endianness from the protein. Bytes are assumed to encode UTF-8characters (and thus—nota bene—the number of characters is notnecessarily the same as the number of bytes).

The ingests offset specifies the number of bytes between the beginningof the protein and the first ingest entry. Each ingest entry comprisesan offset (in offset form) to the next ingest entry, followed again by alength field and a slaw. The ingests offset is functionally identical tothe descrips offset, except that it points to the next ingest entryrather than to the next descrip entry.

In most proteins, every ingest is of the slaw cons type comprising atwo-value list, generally used as a key/value pair. The slaw cons recordcomprises a four-byte length/type header with the second mostsignificant bit set and only the lower 30 bits used to specify length; afour-byte offset to the start of the value (second) element; thefour-byte length of the key element; the slaw record for the keyelement; the four-byte length of the value element; and finally the slawrecord for the value element.

Generally, the cons key is a slaw string. The duplication of data acrossthe several protein and slaw cons length and offsets field provides yetmore opportunity for refinement and optimization.

The construct used under an embodiment to embed typed data insideproteins, as described above, is a tagged byte-sequence specificationand abstraction called a “slaw” (the plural is “slawx”). A slaw is alinear sequence of bytes representing a piece of (possibly aggregate)typed data, and is associated with programming-language-specific APIsthat allow slawx to be created, modified and moved around between memoryspaces, storage media, and machines. The slaw type scheme is intended tobe extensible and as lightweight as possible, and to be a commonsubstrate that can be used from any programming language.

The desire to build an efficient, large-scale inter-processcommunication mechanism is the driver of the slaw configuration.Conventional programming languages provide sophisticated data structuresand type facilities that work well in process-specific memory layouts,but these data representations invariably break down when data needs tobe moved between processes or stored on disk. The slaw architecture is,first, a substantially efficient, multi-platform friendly, low-leveldata model for inter-process communication.

But even more importantly, slawx are configured to influence, togetherwith proteins, and enable the development of future computing hardware(microprocessors, memory controllers, disk controllers). A few specificadditions to, say, the instruction sets of commonly availablemicroprocessors make it possible for slawx to become as efficient evenfor single-process, in-memory data layout as the schema used in mostprogramming languages.

Each slaw comprises a variable-length type header followed by atype-specific data layout. In an example embodiment, which supports fullslaw functionality in C, C++ and Ruby for example, types are indicatedby a universal integer defined in system header files accessible fromeach language. More sophisticated and flexible type resolutionfunctionality is also enabled: for example, indirect typing viauniversal object IDs and network lookup.

The slaw configuration of an embodiment allows slaw records to be usedas objects in language-friendly fashion from both Ruby and C++, forexample. A suite of utilities external to the C++ compiler sanity-checkslaw byte layout, create header files and macros specific to individualslaw types, and auto-generate bindings for Ruby. As a result,well-configured slaw types are quite efficient even when used fromwithin a single process. Any slaw anywhere in a process's accessiblememory can be addressed without a copy or “deserialization” step.

Slaw functionality of an embodiment includes API facilities to performone or more of the following: create a new slaw of a specific type;create or build a language-specific reference to a slaw from bytes ondisk or in memory; embed data within a slaw in type-specific fashion;query the size of a slaw; retrieve data from within a slaw; clone aslaw; and translate the endianness and other format attributes of alldata within a slaw. Every species of slaw implements the abovebehaviors.

FIG. 23B1 and FIG. 23B2 show a slaw header format, under an embodiment.A detailed description of the slaw follows.

The internal structure of each slaw optimizes each of type resolution,access to encapsulated data, and size information for that slawinstance. In an embodiment, the full set of slaw types is by designminimally complete, and includes: the slaw string; the slaw cons (i.e.dyad); the slaw list; and the slaw numerical object, which itselfrepresents a broad set of individual numerical types understood aspermutations of a half-dozen or so basic attributes. The other basicproperty of any slaw is its size. In an embodiment, slawx havebyte-lengths quantized to multiples of four; these four-byte words arereferred to herein as ‘quads’. In general, such quad-based sizing alignsslawx well with the configurations of modern computer hardwarearchitectures.

The first four bytes of every slaw in an embodiment comprise a headerstructure that encodes type-description and other metainformation, andthat ascribes specific type meanings to particular bit patterns. Forexample, the first (most significant) bit of a slaw header is used tospecify whether the size (length in quad-words) of that slaw follows theinitial four-byte type header. When this bit is set, it is understoodthat the size of the slaw is explicitly recorded in the next four bytesof the slaw (e.g., bytes five through eight); if the size of the slaw issuch that it cannot be represented in four bytes (i.e. if the size is oris larger than two to the thirty-second power) then thenext-most-significant bit of the slaw's initial four bytes is also set,which means that the slaw has an eight-byte (rather than four byte)length. In that case, an inspecting process will find the slaw's lengthstored in ordinal bytes five through twelve. On the other hand, thesmall number of slaw types means that in many cases a fully specifiedtypal bit-pattern “leaves unused” many bits in the four byte slawheader; and in such cases these bits may be employed to encode theslaw's length, saving the bytes (five through eight) that wouldotherwise be required.

For example, an embodiment leaves the most significant bit of the slawheader (the “length follows” flag) unset and sets the next bit toindicate that the slaw is a “wee cons”, and in this case the length ofthe slaw (in quads) is encoded in the remaining thirty bits. Similarly,a “wee string” is marked by the pattern 001 in the header, which leavestwenty-nine bits for representation of the slaw-string's length; and aleading 0001 in the header describes a “wee list”, which by virtue ofthe twenty-eight available length-representing bits can be a slaw listof up to two-to-the-twenty-eight quads in size. A “full string” (or consor list) has a different bit signature in the header, with the mostsignificant header bit necessarily set because the slaw length isencoded separately in bytes five through eight (or twelve, in extremecases). Note that the Plasma implementation “decides” at the instant ofslaw construction whether to employ the “wee” or the “full” version ofthese constructs (the decision is based on whether the resulting sizewill “fit” in the available wee bits or not), but the full-vs.-weedetail is hidden from the user of the Plasma implementation, who knowsand cares only that she is using a slaw string, or a slaw cons, or aslaw list.

Numeric slawx are, in an embodiment, indicated by the leading headerpattern 00001. Subsequent header bits are used to represent a set oforthogonal properties that may be combined in arbitrary permutation. Anembodiment employs, but is not limited to, five such character bits toindicate whether or not the number is: (1) floating point; (2) complex;(3) unsigned; (4) “wide”; (5) “stumpy” ((4) “wide” and (5) “stumpy” arepermuted to indicate eight, sixteen, thirty-two, and sixty-four bitnumber representations). Two additional bits (e.g., (7) and (8))indicate that the encapsulated numeric data is a two-, three-, orfour-element vector (with both bits being zero suggesting that thenumeric is a “one-element vector” (i.e. a scalar)). In this embodimentthe eight bits of the fourth header byte are used to encode the size (inbytes, not quads) of the encapsulated numeric data. This size encodingis offset by one, so that it can represent any size between andincluding one and two hundred fifty-six bytes. Finally, two characterbits (e.g., (9) and (10)) are used to indicate that the numeric dataencodes an array of individual numeric entities, each of which is of thetype described by character bits (1) through (8). In the case of anarray, the individual numeric entities are not each tagged withadditional headers, but are packed as continuous data following thesingle header and, possibly, explicit slaw size information.

This embodiment affords simple and efficient slaw duplication (which canbe implemented as a byte-for-byte copy) and extremely straightforwardand efficient slaw comparison (two slawx are the same in this embodimentif and only if there is a one-to-one match of each of their componentbytes considered in sequence). This latter property is important, forexample, to an efficient implementation of the protein architecture, oneof whose critical and pervasive features is the ability to searchthrough or ‘match on’ a protein's descrips list.

Further, the embodiments herein allow aggregate slaw forms (e.g., theslaw cons and the slaw list) to be constructed simply and efficiently.For example, an embodiment builds a slaw cons from two component slawx,which may be of any type, including themselves aggregates, by: (a)querying each component slaw's size; (b) allocating memory of size equalto the sum of the sizes of the two component slawx and the one, two, orthree quads needed for the header-plus-size structure; (c) recording theslaw header (plus size information) in the first four, eight, or twelvebytes; and then (d) copying the component slawx's bytes in turn into theimmediately succeeding memory. Significantly, such a constructionroutine need know nothing about the types of the two component slawx;only their sizes (and accessibility as a sequence of bytes) matters. Thesame process pertains to the construction of slaw lists, which areordered encapsulations of arbitrarily many sub-slawx of (possibly)heterogeneous type.

A further consequence of the slaw system's fundamental format assequential bytes in memory obtains in connection with “traversal”activities—a recurring use pattern uses, for example, sequential accessto the individual slawx stored in a slaw list. The individual slawx thatrepresent the descrips and ingests within a protein structure mustsimilarly be traversed. Such maneuvers are accomplished in a stunninglystraightforward and efficient manner: to “get to” the next slaw in aslaw list, one adds the length of the current slaw to its location inmemory, and the resulting memory location is identically the header ofthe next slaw. Such simplicity is possible because the slaw and proteindesign eschews “indirection”; there are no pointers; rather, the datasimply exists, in its totality, in situ.

To the point of slaw comparison, a complete implementation of the Plasmasystem must acknowledge the existence of differing and incompatible datarepresentation schemes across and among different operating systems,CPUs, and hardware architectures. Major such differences includebyte-ordering policies (e.g., little-vs. big-endianness) andfloating-point representations; other differences exist. The Plasmaspecification requires that the data encapsulated by slawx be guaranteedinterprable (i.e., must appear in the native format of the architectureor platform from which the slaw is being inspected. This requirementmeans in turn that the Plasma system is itself responsible for dataformat conversion. However, the specification stipulates only that theconversion take place before a slaw becomes “at all visible” to anexecuting process that might inspect it. It is therefore up to theindividual implementation at which point it chooses to perform suchformat c conversion; two appropriate approaches are that slaw datapayloads are conformed to the local architecture's data format (1) as anindividual slaw is “pulled out” of a protein in which it had beenpacked, or (2) for all slaw in a protein simultaneously, as that proteinis extracted from the pool in which it was resident. Note that theconversion stipulation considers the possibility of hardware-assistedimplementations. For example, networking chipsets built with explicitPlasma capability may choose to perform format conversion intelligentlyand at the “instant of transmission”, based on the known characteristicsof the receiving system. Alternately, the process of transmission mayconvert data payloads into a canonical format, with the receivingprocess symmetrically converting from canonical to “local” format.Another embodiment performs format conversion “at the metal”, meaningthat data is always stored in canonical format, even in local memory,and that the memory controller hardware itself performs the conversionas data is retrieved from memory and placed in the registers of theproximal CPU.

A minimal (and read-only) protein implementation of an embodimentincludes operation or behavior in one or more applications orprogramming languages making use of proteins. FIG. 23C is a flow diagram650 for using proteins, under an embodiment. Operation begins byquerying 652 the length in bytes of a protein. The number of descripsentries is queried 654. The number of ingests is queried 656. A descripentry is retrieved 658 by index number. An ingest is retrieved 660 byindex number.

The embodiments described herein also define basic methods allowingproteins to be constructed and filled with data, helper-methods thatmake common tasks easier for programmers, and hooks for creatingoptimizations. FIG. 23D is a flow diagram 670 for constructing orgenerating proteins, under an embodiment. Operation begins with creation672 of a new protein. A series of descrips entries are appended 674. Aningest is also appended 676. The presence of a matching descrip isqueried 678, and the presence of a matching ingest key is queried 680.Given an ingest key, an ingest value is retrieved 682. Pattern matchingis performed 684 across descrips. Non-structured metadata is embedded686 near the beginning of the protein.

As described above, slawx provide the lowest-level of data definitionfor inter-process exchange, proteins provide mid-level structure andhooks for querying and filtering, and pools provide for high-levelorganization and access semantics. The pool is a repository forproteins, providing linear sequencing and state caching. The pool alsoprovides multi-process access by multiple programs or applications ofnumerous different types. Moreover, the pool provides a set of common,optimizable filtering and pattern-matching behaviors.

The pools of an embodiment, which can accommodate tens of thousands ofproteins, function to maintain state, so that individual processes canoffload much of the tedious bookkeeping common to multi-process programcode. A pool maintains or keeps a large buffer of past proteinsavailable—the Platonic pool is explicitly infinite—so that participatingprocesses can scan both backwards and forwards in a pool at will. Thesize of the buffer is implementation dependent, of course, but in commonusage it is often possible to keep proteins in a pool for hours or days.

The most common style of pool usage as described herein hews to abiological metaphor, in contrast to the mechanistic, point-to-pointapproach taken by existing inter-process communication frameworks. Thename protein alludes to biological inspiration: data proteins in poolsare available for flexible querying and pattern matching by a largenumber of computational processes, as chemical proteins in a livingorganism are available for pattern matching and filtering by largenumbers of cellular agents.

Two additional abstractions lean on the biological metaphor, includinguse of “handlers”, and the Golgi framework. A process that participatesin a pool generally creates a number of handlers. Handlers arerelatively small bundles of code that associate match conditions withhandle behaviors. By tying one or more handlers to a pool, a processsets up flexible call-back triggers that encapsulate state and react tonew proteins.

A process that participates in several pools generally inherits from anabstract Golgi class. The Golgi framework provides a number of usefulroutines for managing multiple pools and handlers. The Golgi class alsoencapsulates parent-child relationships, providing a mechanism for localprotein exchange that does not use a pool.

A pools API provided under an embodiment is configured to allow pools tobe implemented in a variety of ways, in order to account both forsystem-specific goals and for the available capabilities of givenhardware and network architectures. The two fundamental systemprovisions upon which pools depend are a storage facility and a means ofinter-process communication. The extant systems described herein use aflexible combination of shared memory, virtual memory, and disk for thestorage facility, and IPC queues and TCP/IP sockets for inter-processcommunication.

Pool functionality of an embodiment includes, but is not limited to, thefollowing: participating in a pool; placing a protein in a pool;retrieving the next unseen protein from a pool; rewinding orfast-forwarding through the contents (e.g., proteins) within a pool.Additionally, pool functionality can include, but is not limited to, thefollowing: setting up a streaming pool call-back for a process;selectively retrieving proteins that match particular patterns ofdescrips or ingests keys; scanning backward and forwards for proteinsthat match particular patterns of descrips or ingests keys.

The proteins described above are provided to pools as a way of sharingthe protein data contents with other applications. FIG. 24 is a blockdiagram of a processing environment including data exchange using slawx,proteins, and pools, under an embodiment. This example environmentincludes three devices (e.g., Device X, Device Y, and Device Z,collectively referred to herein as the “devices”) sharing data throughthe use of slawx, proteins and pools as described above. Each of thedevices is coupled to the three pools (e.g., Pool 1, Pool 2, Pool 3).Pool 1 includes numerous proteins (e.g., Protein X1, Protein Z2, ProteinY2, Protein X4, Protein Y4) contributed or transferred to the pool fromthe respective devices (e.g., protein Z2 is transferred or contributedto pool 1 by device Z, etc.). Pool 2 includes numerous proteins (e.g.,Protein Z4, Protein Y3, Protein Z1, Protein X3) contributed ortransferred to the pool from the respective devices (e.g., protein Y3 istransferred or contributed to pool 2 by device Y, etc.). Pool 3 includesnumerous proteins (e.g., Protein Y1, Protein Z3, Protein X2) contributedor transferred to the pool from the respective devices (e.g., protein X2is transferred or contributed to pool 3 by device X, etc.). While theexample described above includes three devices coupled or connectedamong three pools, any number of devices can be coupled or connected inany manner or combination among any number of pools, and any pool caninclude any number of proteins contributed from any number orcombination of devices. The proteins and pools of this example are asdescribed above with reference to FIGS. 18-23.

FIG. 25 is a block diagram of a processing environment includingmultiple devices and numerous programs running on one or more of thedevices in which the Plasma constructs (e.g., pools, proteins, and slaw)are used to allow the numerous running programs to share andcollectively respond to the events generated by the devices, under anembodiment. This system is but one example of a multi-user,multi-device, multi-computer interactive control scenario orconfiguration. More particularly, in this example, an interactivesystem, comprising multiple devices (e.g., device A, B, etc.) and anumber of programs (e.g., apps AA-AX, apps BA-BX, etc.) running on thedevices uses the Plasma constructs (e.g., pools, proteins, and slaw) toallow the running programs to share and collectively respond to theevents generated by these input devices.

In this example, each device (e.g., device A, B, etc.) translatesdiscrete raw data generated by or output from the programs (e.g., appsAA-AX, apps BA-BX, etc.) running on that respective device into Plasmaproteins and deposits those proteins into a Plasma pool. For example,program AX generates data or output and provides the output to device Awhich, in turn, translates the raw data into proteins (e.g., protein 1A,protein 2A, etc.) and deposits those proteins into the pool. As anotherexample, program BC generates data and provides the data to device Bwhich, in turn, translates the data into proteins (e.g., protein 1B,protein 2B, etc.) and deposits those proteins into the pool.

Each protein contains a descrip list that specifies the data or outputregistered by the application as well as identifying information for theprogram itself. Where possible, the protein descrips may also ascribe ageneral semantic meaning for the output event or action. The protein'sdata payload (e.g., ingests) carries the full set of useful stateinformation for the program event.

The proteins, as described above, are available in the pool for use byany program or device coupled or connected to the pool, regardless oftype of the program or device. Consequently, any number of programsrunning on any number of computers may extract event proteins from theinput pool. These devices need only be able to participate in the poolvia either the local memory bus or a network connection in order toextract proteins from the pool. An immediate consequence of this is thebeneficial possibility of decoupling processes that are responsible forgenerating processing events from those that use or interpret theevents. Another consequence is the multiplexing of sources and consumersof events so that devices may be controlled by one person or may be usedsimultaneously by several people (e.g., a Plasma-based input frameworksupports many concurrent users), while the resulting event streams arein turn visible to multiple event consumers.

As an example, device C can extract one or more proteins (e.g., protein1A, protein 2A, etc.) from the pool. Following protein extraction,device C can use the data of the protein, retrieved or read from theslaw of the descrips and ingests of the protein, in processing events towhich the protein data corresponds. As another example, device B canextract one or more proteins (e.g., protein 1C, protein 2A, etc.) fromthe pool. Following protein extraction, device B can use the data of theprotein in processing events to which the protein data corresponds.

Devices and/or programs coupled or connected to a pool may skimbackwards and forwards in the pool looking for particular sequences ofproteins. It is often useful, for example, to set up a program to waitfor the appearance of a protein matching a certain pattern, then skimbackwards to determine whether this protein has appeared in conjunctionwith certain others. This facility for making use of the stored eventhistory in the input pool often makes writing state management codeunnecessary, or at least significantly reduces reliance on suchundesirable coding patterns.

FIG. 26 is a block diagram of a processing environment includingmultiple devices and numerous programs running on one or more of thedevices in which the Plasma constructs (e.g., pools, proteins, and slaw)are used to allow the numerous running programs to share andcollectively respond to the events generated by the devices, under analternative embodiment. This system is but one example of a multi-user,multi-device, multi-computer interactive control scenario orconfiguration. More particularly, in this example, an interactivesystem, comprising multiple devices (e.g., devices X and Y coupled todevices A and B, respectively) and a number of programs (e.g., appsAA-AX, apps BA-BX, etc.) running on one or more computers (e.g., deviceA, device B, etc.) uses the Plasma constructs (e.g., pools, proteins,and slaw) to allow the running programs to share and collectivelyrespond to the events generated by these input devices.

In this example, each device (e.g., devices X and Y coupled to devices Aand B, respectively) is managed and/or coupled to run under or inassociation with one or more programs hosted on the respective device(e.g., device A, device B, etc.) which translates the discrete raw datagenerated by the device (e.g., device X, device A, device Y, device B,etc.) hardware into Plasma proteins and deposits those proteins into aPlasma pool. For example, device X running in association withapplication AB hosted on device A generates raw data, translates thediscrete raw data into proteins (e.g., protein 1A, protein 2A, etc.) anddeposits those proteins into the pool. As another example, device Xrunning in association with application AT hosted on device A generatesraw data, translates the discrete raw data into proteins (e.g., protein1A, protein 2A, etc.) and deposits those proteins into the pool. As yetanother example, device Z running in association with application CDhosted on device C generates raw data, translates the discrete raw datainto proteins (e.g., protein 1C, protein 2C, etc.) and deposits thoseproteins into the pool.

Each protein contains a descrip list that specifies the actionregistered by the input device as well as identifying information forthe device itself. Where possible, the protein descrips may also ascribea general semantic meaning for the device action. The protein's datapayload (e.g., ingests) carries the full set of useful state informationfor the device event.

The proteins, as described above, are available in the pool for use byany program or device coupled or connected to the pool, regardless oftype of the program or device. Consequently, any number of programsrunning on any number of computers may extract event proteins from theinput pool. These devices need only be able to participate in the poolvia either the local memory bus or a network connection in order toextract proteins from the pool. An immediate consequence of this is thebeneficial possibility of decoupling processes that are responsible forgenerating processing events from those that use or interpret theevents. Another consequence is the multiplexing of sources and consumersof events so that input devices may be controlled by one person or maybe used simultaneously by several people (e.g., a Plasma-based inputframework supports many concurrent users), while the resulting eventstreams are in turn visible to multiple event consumers.

Devices and/or programs coupled or connected to a pool may skimbackwards and forwards in the pool looking for particular sequences ofproteins. It is often useful, for example, to set up a program to waitfor the appearance of a protein matching a certain pattern, then skimbackwards to determine whether this protein has appeared in conjunctionwith certain others. This facility for making use of the stored eventhistory in the input pool often makes writing state management codeunnecessary, or at least significantly reduces reliance on suchundesirable coding patterns.

FIG. 27 is a block diagram of a processing environment includingmultiple input devices coupled among numerous programs running on one ormore of the devices in which the Plasma constructs (e.g., pools,proteins, and slaw) are used to allow the numerous running programs toshare and collectively respond to the events generated by the inputdevices, under another alternative embodiment. This system is but oneexample of a multi-user, multi-device, multi-computer interactivecontrol scenario or configuration. More particularly, in this example,an interactive system, comprising multiple input devices (e.g., inputdevices A, B, BA, and BB, etc.) and a number of programs (not shown)running on one or more computers (e.g., device A, device B, etc.) usesthe Plasma constructs (e.g., pools, proteins, and slaw) to allow therunning programs to share and collectively respond to the eventsgenerated by these input devices.

In this example, each input device (e.g., input devices A, B, BA, andBB, etc.) is managed by a software driver program hosted on therespective device (e.g., device A, device B, etc.) which translates thediscrete raw data generated by the input device hardware into Plasmaproteins and deposits those proteins into a Plasma pool. For example,input device A generates raw data and provides the raw data to device Awhich, in turn, translates the discrete raw data into proteins (e.g.,protein 1A, protein 2A, etc.) and deposits those proteins into the pool.As another example, input device BB generates raw data and provides theraw data to device B which, in turn, translates the discrete raw datainto proteins (e.g., protein 1B, protein 3B, etc.) and deposits thoseproteins into the pool.

Each protein contains a descrip list that specifies the actionregistered by the input device as well as identifying information forthe device itself. Where possible, the protein descrips may also ascribea general semantic meaning for the device action. The protein's datapayload (e.g., ingests) carries the full set of useful state informationfor the device event.

To illustrate, here are example proteins for two typical events in sucha system. Proteins are represented here as text however, in an actualimplementation, the constituent parts of these proteins are typed databundles (e.g., slaw). The protein describing a g-speak “one fingerclick” pose (described in the Related Applications) is as follows:

[ Descrips: { point, engage, one, one-finger-engage, hand,  pilot-id-02, hand-id-23 }  Ingests: { pilot-id => 02,   hand-id => 23,  pos => [ 0.0, 0.0, 0.0 ]   angle-axis => [ 0.0, 0.0, 0.0, 0.707 ]  gripe => ..{circumflex over ( )}∥:vx   time => 184437103.29}]As a further example, the protein describing a mouse click is asfollows:

[ Descrips:{ point, click, one, mouse-click, button-one,   mouse-id-02 } Ingests: { mouse-id => 23,   pos => [ 0.0, 0.0, 0.0 ]   time =>184437124.80}]

Either or both of the sample proteins foregoing might cause aparticipating program of a host device to run a particular portion ofits code. These programs may be interested in the general semanticlabels: the most general of all, “point”, or the more specific pair,“engage, one”. Or they may be looking for events that would plausibly begenerated only by a precise device: “one-finger-engage”, or even asingle aggregate object, “hand-id-23”.

The proteins, as described above, are available in the pool for use byany program or device coupled or connected to the pool, regardless oftype of the program or device. Consequently, any number of programsrunning on any number of computers may extract event proteins from theinput pool. These devices need only be able to participate in the poolvia either the local memory bus or a network connection in order toextract proteins from the pool. An immediate consequence of this is thebeneficial possibility of decoupling processes that are responsible forgenerating ‘input events’ from those that use or interpret the events.Another consequence is the multiplexing of sources and consumers ofevents so that input devices may be controlled by one person or may beused simultaneously by several people (e.g., a Plasma-based inputframework supports many concurrent users), while the resulting eventstreams are in turn visible to multiple event consumers.

As an example or protein use, device C can extract one or more proteins(e.g., protein 1B, etc.) from the pool. Following protein extraction,device C can use the data of the protein, retrieved or read from theslaw of the descrips and ingests of the protein, in processing inputevents of input devices CA and CC to which the protein data corresponds.As another example, device A can extract one or more proteins (e.g.,protein 1B, etc.) from the pool. Following protein extraction, device Acan use the data of the protein in processing input events of inputdevice A to which the protein data corresponds.

Devices and/or programs coupled or connected to a pool may skimbackwards and forwards in the pool looking for particular sequences ofproteins. It is often useful, for example, to set up a program to waitfor the appearance of a protein matching a certain pattern, then skimbackwards to determine whether this protein has appeared in conjunctionwith certain others. This facility for making use of the stored eventhistory in the input pool often makes writing state management codeunnecessary, or at least significantly reduces reliance on suchundesirable coding patterns.

Examples of input devices that are used in the embodiments of the systemdescribed herein include gestural input sensors, keyboards, mice,infrared remote controls such as those used in consumer electronics, andtask-oriented tangible media objects, to name a few.

FIG. 28 is a block diagram of a processing environment includingmultiple devices coupled among numerous programs running on one or moreof the devices in which the Plasma constructs (e.g., pools, proteins,and slaw) are used to allow the numerous running programs to share andcollectively respond to the graphics events generated by the devices,under yet another alternative embodiment. This system is but one exampleof a system comprising multiple running programs (e.g. graphics A-E) andone or more display devices (not shown), in which the graphical outputof some or all of the programs is made available to other programs in acoordinated manner using the Plasma constructs (e.g., pools, proteins,and slaw) to allow the running programs to share and collectivelyrespond to the graphics events generated by the devices.

It is often useful for a computer program to display graphics generatedby another program. Several common examples include video conferencingapplications, network-based slideshow and demo programs, and windowmanagers. Under this configuration, the pool is used as a Plasma libraryto implement a generalized framework which encapsulates video, networkapplication sharing, and window management, and allows programmers toadd in a number of features not commonly available in current versionsof such programs.

Programs (e.g., graphics A-E) running in the Plasma compositingenvironment participate in a coordination pool through couplings and/orconnections to the pool. Each program may deposit proteins in that poolto indicate the availability of graphical sources of various kinds.Programs that are available to display graphics also deposit proteins toindicate their displays' capabilities, security and user profiles, andphysical and network locations.

Graphics data also may be transmitted through pools, or display programsmay be pointed to network resources of other kinds (RTSP streams, forexample). The phrase “graphics data” as used herein refers to a varietyof different representations that lie along a broad continuum; examplesof graphics data include but are not limited to literal examples (e.g.,an ‘image’, or block of pixels), procedural examples (e.g., a sequenceof ‘drawing’ directives, such as those that flow down a typical openGLpipeline), and descriptive examples (e.g., instructions that combineother graphical constructs by way of geometric transformation, clipping,and compositing operations).

On a local machine graphics data may be delivered throughplatform-specific display driver optimizations. Even when graphics arenot transmitted via pools, often a periodic screen-capture will bestored in the coordination pool so that clients without direct access tothe more esoteric sources may still display fall-back graphics.

One advantage of the system described here is that unlike most messagepassing frameworks and network protocols, pools maintain a significantbuffer of data. So programs can rewind backwards into a pool looking ataccess and usage patterns (in the case of the coordination pool) orextracting previous graphics frames (in the case of graphics pools).

FIG. 29 is a block diagram of a processing environment includingmultiple devices coupled among numerous programs running on one or moreof the devices in which the Plasma constructs (e.g., pools, proteins,and slaw) are used to allow stateful inspection, visualization, anddebugging of the running programs, under still another alternativeembodiment. This system is but one example of a system comprisingmultiple running programs (e.g. program P-A, program P-B, etc.) onmultiple devices (e.g., device A, device B, etc.) in which some programsaccess the internal state of other programs using or via pools.

Most interactive computer systems comprise many programs runningalongside one another, either on a single machine or on multiplemachines and interacting across a network. Multi-program systems can bedifficult to configure, analyze and debug because run-time data ishidden inside each process and difficult to access. The generalizedframework and Plasma constructs of an embodiment described herein allowrunning programs to make much of their data available via pools so thatother programs may inspect their state. This framework enables debuggingtools that are more flexible than conventional debuggers, sophisticatedsystem maintenance tools, and visualization harnesses configured toallow human operators to analyze in detail the sequence of states that aprogram or programs has passed through.

Referring to FIG. 29, a program (e.g., program P-A, program P-B, etc.)running in this framework generates or creates a process pool uponprogram start up. This pool is registered in the system almanac, andsecurity and access controls are applied. More particularly, each device(e.g., device A, B, etc.) translates discrete raw data generated by oroutput from the programs (e.g., program P-A, program P-B, etc.) runningon that respective device into Plasma proteins and deposits thoseproteins into a Plasma pool. For example, program P-A generates data oroutput and provides the output to device A which, in turn, translatesthe raw data into proteins (e.g., protein 1A, protein 2A, protein 3A,etc.) and deposits those proteins into the pool. As another example,program P-B generates data and provides the data to device B which, inturn, translates the data into proteins (e.g., proteins 1B-4B, etc.) anddeposits those proteins into the pool.

For the duration of the program's lifetime, other programs withsufficient access permissions may attach to the pool and read theproteins that the program deposits; this represents the basic inspectionmodality, and is a conceptually “one-way” or “read-only” proposition:entities interested in a program P-A inspect the flow of statusinformation deposited by P-A in its process pool. For example, aninspection program or application running under device C can extract oneor more proteins (e.g., protein 1A, protein 2A, etc.) from the pool.Following protein extraction, device C can use the data of the protein,retrieved or read from the slaw of the descrips and ingests of theprotein, to access, interpret and inspect the internal state of programP-A.

But, recalling that the Plasma system is not only an efficient statefultransmission scheme but also an omnidirectional messaging environment,several additional modes support program-to-program state inspection. Anauthorized inspection program may itself deposit proteins into programP's process pool to influence or control the characteristics of stateinformation produced and placed in that process pool (which, after all,program P not only writes into but reads from).

FIG. 30 is a block diagram of a processing environment includingmultiple devices coupled among numerous programs running on one or moreof the devices in which the Plasma constructs (e.g., pools, proteins,and slaw) are used to allow influence or control the characteristics ofstate information produced and placed in that process pool, under anadditional alternative embodiment. In this system example, theinspection program of device C can for example request that programs(e.g., program P-A, program P-B, etc.) dump more state than normal intothe pool, either for a single instant or for a particular duration. Or,prefiguring the next ‘level’ of debug communication, an interestedprogram can request that programs (e.g., program P-A, program P-B, etc.)emit a protein listing the objects extant in its runtime environmentthat are individually capable of and available for interaction via thedebug pool. Thus informed, the interested program can ‘address’individuals among the objects in the programs runtime, placing proteinsin the process pool that a particular object alone will take up andrespond to. The interested program might, for example, request that anobject emit a report protein describing the instantaneous values of allits component variables. Even more significantly, the interested programcan, via other proteins, direct an object to change its behavior or itsvariables' values.

More specifically, in this example, inspection application of device Cplaces into the pool a request (in the form of a protein) for an objectlist (e.g., “Request-Object List”) that is then extracted by each device(e.g., device A, device B, etc.) coupled to the pool. In response to therequest, each device (e.g., device A, device B, etc.) places into thepool a protein (e.g., protein 1A, protein 1B, etc.) listing the objectsextant in its runtime environment that are individually capable of andavailable for interaction via the debug pool.

Thus informed via the listing from the devices, and in response to thelisting of the objects, the inspection application of device C addressesindividuals among the objects in the programs runtime, placing proteinsin the process pool that a particular object alone will take up andrespond to. The inspection application of device C can, for example,place a request protein (e.g., protein “Request Report P-A-O”, “RequestReport P-B-O”) in the pool that an object (e.g., object P-A-O, objectP-B-O, respectively) emit a report protein (e.g., protein 2A, protein2B, etc.) describing the instantaneous values of all its componentvariables. Each object (e.g., object P-A-O, object P-B-O) extracts itsrequest (e.g., protein “Request Report P-A-O”, “Request Report P-B-O”,respectively) and, in response, places a protein into the pool thatincludes the requested report (e.g., protein 2A, protein 2B,respectively). Device C then extracts the various report proteins (e.g.,protein 2A, protein 2B, etc.) and takes subsequent processing action asappropriate to the contents of the reports.

In this way, use of Plasma as an interchange medium tends ultimately toerode the distinction between debugging, process control, andprogram-to-program communication and coordination.

To that last, the generalized Plasma framework allows visualization andanalysis programs to be designed in a loosely-coupled fashion. Avisualization tool that displays memory access patterns, for example,might be used in conjunction with any program that outputs its basicmemory reads and writes to a pool. The programs undergoing analysis neednot know of the existence or design of the visualization tool, and viceversa.

The use of pools in the manners described above does not unduly affectsystem performance. For example, embodiments have allowed for depositingof several hundred thousand proteins per second in a pool, so thatenabling even relatively verbose data output does not noticeably inhibitthe responsiveness or interactive character of most programs.

Embodiments described herein include a method comprising collating inputdata from a plurality of sources. The input data is semanticallyuncorrelated three-space data of an instantaneous spatial and geometricstate of an object in a frame of reference of the object. The method ofan embodiment comprises conforming the input data into a stream ofspatiotemporal data. The spatiotemporal data of the stream is uniformlyrepresented. The method of an embodiment comprises generating gesturalevents from the spatiotemporal data using a plurality of gesturedescriptions. The method of an embodiment comprises representing thegestural events in a protoevent comprising a data format that isapplication-neutral and fully articulated. The method of an embodimentcomprises distributing the gestural events and providing access to thegestural events via corresponding protoevents by at least one eventconsumer in a spatial-semantic frame of reference of the at least oneevent consumer.

Embodiments described herein include a method comprising: collatinginput data from a plurality of sources, wherein the input data issemantically uncorrelated three-space data of an instantaneous spatialand geometric state of an object in a frame of reference of the object;conforming the input data into a stream of spatiotemporal data, whereinthe spatiotemporal data of the stream is uniformly represented;generating gestural events from the spatiotemporal data using aplurality of gesture descriptions; representing the gestural events in aprotoevent comprising a data format that is application-neutral andfully articulated; and distributing the gestural events and providingaccess to the gestural events via corresponding protoevents by at leastone event consumer in a spatial-semantic frame of reference of the atleast one event consumer.

The input data of an embodiment comprises unconstrained freespacegestural data of the object.

The input data of an embodiment comprises proximal gestural data of theobject when the object is at least one of within a proximate rangerelative to a surface and within a defined volume.

The input data of an embodiment comprises hover gestural data of theobject when the object is within a plane adjacent to a surface.

The input data of an embodiment comprises surface-contact gestural dataof the object when the object is in contact with a surface.

The input data of an embodiment comprises a plurality of data streams.

The method of an embodiment comprises temporally aligning the pluralityof data streams.

The method of an embodiment comprises spatially seaming events from theplurality of data streams and generating a single synthetic event.

The method of an embodiment comprises performing semantic aggregationincluding collecting relevant events resulting from precedingoperations.

The method of an embodiment comprises performing metainformationtagging.

The method of an embodiment comprises receiving the input data from atleast one of an optical motion-tracking system, a time-of-flighttracking system, an electric field sensing system, and a touch screendevice.

The method of an embodiment comprises receiving the input data from anoptical motion-tracking system.

The method of an embodiment comprises receiving the input data from atime-of-flight tracking system.

The method of an embodiment comprises receiving the input data from atouch screen device.

The method of an embodiment comprises receiving the input data from anelectric field sensing system.

The method of an embodiment comprises receiving the input data from acapacitive sensing system.

The method of an embodiment comprises receiving the spatiotemporal datacomprising three-space position of the object.

The method of an embodiment comprises receiving the spatiotemporal datacomprising three-space orientation of the object.

The method of an embodiment comprises receiving the spatiotemporal datacomprising motion of the object.

The method of an embodiment comprises receiving the spatiotemporal datacomprising bulk three-space position of at least one of a plurality ofelements comprising the object and a plurality of elements coupled tothe object.

The method of an embodiment comprises receiving the spatiotemporal datacomprising bulk three-space orientation of at least one of a pluralityof elements comprising the object and a plurality of elements coupled tothe object.

The method of an embodiment comprises receiving the spatiotemporal datacomprising bulk motion of at least one of a plurality of elementscomprising the object and a plurality of elements coupled to the object.

The method of an embodiment comprises receiving the spatiotemporal datacomprising a semantic digest of a pose of a plurality of elementscomprising the object.

The method of an embodiment comprises comparing the spatiotemporal datato the gesture descriptions.

The method of an embodiment comprises generating the protoevent inresponse to a match between spatiotemporal data and a gesturedescription, wherein the protoevent includes a data format comprising adigest of matched spatiotemporal data, interpreted in a semantic contextof matched gestural descriptions.

The method of an embodiment comprises providing a plurality ofrecognizers, wherein each recognizer comprises a gesture description.

The method of an embodiment comprises performing a plurality ofoperations on the plurality of recognizers, wherein the plurality ofoperations comprise ranking recognizers.

The method of an embodiment comprises performing a plurality ofoperations on the plurality of recognizers, wherein the plurality ofoperations comprise adding recognizers.

The method of an embodiment comprises performing a plurality ofoperations on the plurality of recognizers, wherein the plurality ofoperations comprise removing recognizers.

The method of an embodiment comprises performing a plurality ofoperations on the plurality of recognizers, wherein the plurality ofoperations comprise modifying recognizers.

The method of an embodiment comprises performing a plurality ofoperations on the plurality of recognizers, wherein the plurality ofoperations comprise reconfiguring recognizers.

A recognizer of an embodiment remains dormant prior to a match betweenspatiotemporal data and activation criteria of the recognizer.

A recognizer of an embodiment becomes active when geometric andspatiotemporal aspects of the spatiotemporal data match the activationcriteria.

The recognizer of an embodiment remains active as long as thespatiotemporal data satisfy maintenance criteria of the recognizer.

The recognizer of an embodiment becomes inactive when the spatiotemporaldata fail to satisfy the maintenance criteria.

The method of an embodiment comprises depositing the protoevents in atleast one repository for access by the at least one event consumer.

The method of an embodiment comprises providing a list of the at leastone event consumer.

The method of an embodiment comprises asynchronously transmitting eachprotoevent generated to each of the at least one event consumers.

The method of an embodiment comprises synchronously transmitting eachprotoevent generated to each of the at least one event consumers.

The method of an embodiment comprises transforming the gestural eventsamong a plurality of spatial-semantic frames of reference correspondingto a plurality of event consumers.

The method of an embodiment comprises re-rendering the gestural eventsin a spatial-semantic frame of reference of the at least one eventconsumer.

The method of an embodiment comprises generating the protoevent bygenerating at least one data sequence comprising gestural event dataspecifying the gestural event and state information of the gesturalevent, and forming a data capsule to include the at least one datasequence, the data capsule having a data structure comprising anapplication-independent representation of the at least one datasequence.

The generating of the at least one data sequence comprises generating afirst respective data set that includes first respective gestural eventdata. The generating of the at least one data sequence comprisesgenerating a second respective data set that includes second respectivestate information. The generating of the at least one data sequencecomprises forming a first data sequence to include the first respectivedata set and the second respective data set.

The generating of the at least one data sequence comprises generating afirst respective data set that includes first respective gestural eventdata. The generating of the at least one data sequence comprisesgenerating a second respective data set that includes second respectivestate information. The generating of the at least one data sequencecomprises forming a second data sequence to include the first respectivedata set and the second respective data set.

The generating of the first respective data set of an embodimentincludes generating a first respective data set offset, wherein thefirst respective data set offset points to the first respective data setof the second data sequence.

The generating of the second respective data set of an embodimentincludes generating a second respective data set offset, wherein thesecond respective data set offset points to the second respective dataset of the second data sequence.

The first respective data set of an embodiment is a description list,the description list including a description of the data.

The method of an embodiment comprises generating at least one offset.The method of an embodiment comprises forming the data capsule toinclude the at least one offset.

The method of an embodiment comprises generating a first offset having afirst variable length. The first offset points to the gestural eventdata of a first data sequence of the at least one data sequence.

The method of an embodiment comprises generating a second offset havinga second variable length. The second offset points to the stateinformation of a first data sequence of the at least one data sequence.

The method of an embodiment comprises forming a first code path throughthe data capsule using a first offset of the at least one offset. Themethod of an embodiment comprises forming a second code path through thedata capsule using a second offset of the at least one offset. The firstcode path and the second code path are different paths.

At least one of the first offset and the second offset of an embodimentinclude metadata, the metadata comprising context-specific metadata.

The at least one event consumer of an embodiment is at least oneinteractive system of a plurality of interactive systems, wherein theplurality of interactive systems comprise a plurality of frames ofreference.

The at least one event consumer of an embodiment consumes theprotoevents using an application type specific to the at least one eventconsumer.

The at least one event consumer of an embodiment comprises a firstinteractive system having a first frame of reference and a secondinteractive system having a second frame of reference.

The first interactive system of an embodiment consumes the protoeventsusing a first application type and the second interactive systemconsumes the protoevents using a second application type.

The object of an embodiment is a human hand.

The object of an embodiment is at least one finger of a human hand.

The object of an embodiment includes at least one human hand and atleast one finger of a human hand.

Embodiments described herein include a method comprising collating inputdata from a plurality of sources. The input data is semanticallyuncorrelated three-space data corresponding to an object. The pluralityof sources comprises disparate sources. The method of an embodimentcomprises rendering a plurality of spatial events of the object from theinput data. The plurality of spatial events comprises aconformed-coordinate representation relative to a global room space. Themethod of an embodiment comprises generating aggregates of the spatialevents from the spatial events. The aggregates are logical aggregatesincluding literal geometric and semantic characteristics of the object.The method of an embodiment comprises detecting and disambiguatinggestures from the aggregates of the spatial events. The method of anembodiment comprises generating data bundles representing the gestures.The data bundles are neutrally descriptive. The method of an embodimentcomprises distributing the data bundles for consumption by a pluralityof disparate applications.

Embodiments described herein include a method comprising: collatinginput data from a plurality of sources, wherein the input data issemantically uncorrelated three-space data corresponding to an object,wherein the plurality of sources comprise disparate sources; rendering aplurality of spatial events of the object from the input data, whereinthe plurality of spatial events comprise a conformed-coordinaterepresentation relative to a global room space; generating aggregates ofthe spatial events from the spatial events, wherein the aggregates arelogical aggregates including literal geometric and semanticcharacteristics of the object; detecting and disambiguating gesturesfrom the aggregates of the spatial events; generating data bundlesrepresenting the gestures, wherein the data bundles are neutrallydescriptive; and distributing the data bundles for consumption by aplurality of disparate applications.

Embodiments described herein include a system comprising a data funnelcoupled to a processor. The data funnel collates input data from aplurality of sources. The input data is semantically uncorrelatedthree-space data of an instantaneous spatial and geometric state of anobject in a frame of reference of the object. The plurality of sourcescomprises disparate sources. The data funnel conforms the input datainto a stream of spatiotemporal data. The spatiotemporal data of thestream is uniformly represented. The system of an embodiment comprises agesture engine coupled to the data funnel. The gesture engine generatesgestural events from the spatiotemporal data using a plurality ofgesture descriptions. The gesture engine represents the gestural eventsin a protoevent comprising a data format that is application-neutral andfully articulated. The system of an embodiment comprises a distributorcoupled to the gesture engine. The distributor provides access to thegestural events by at least one event consumer via correspondingprotoevents in a spatial-semantic frame of reference of the at least oneevent consumer.

Embodiments described herein include a system comprising: a data funnelcoupled to a processor, wherein the data funnel collates input data froma plurality of sources, wherein the input data is semanticallyuncorrelated three-space data of an instantaneous spatial and geometricstate of an object in a frame of reference of the object, wherein theplurality of sources comprise disparate sources, wherein the data funnelconforms the input data into a stream of spatiotemporal data, whereinthe spatiotemporal data of the stream is uniformly represented; agesture engine coupled to the data funnel, wherein the gesture enginegenerates gestural events from the spatiotemporal data using a pluralityof gesture descriptions, wherein the gesture engine represents thegestural events in a protoevent comprising a data format that isapplication-neutral and fully articulated; and a distributor coupled tothe gesture engine, wherein the distributor provides access to thegestural events by at least one event consumer via correspondingprotoevents in a spatial-semantic frame of reference of the at least oneevent consumer.

The input data of an embodiment comprises unconstrained freespacegestural data of the object.

The input data of an embodiment comprises proximal gestural data of theobject when the object is at least one of within a proximate rangerelative to a surface and within a defined volume.

The input data of an embodiment comprises hover gestural data of theobject when the object is within a space immediately adjacent to asurface.

The input data of an embodiment comprises surface-contact gestural dataof the object when the object is in contact with a surface.

The input data of an embodiment comprises a plurality of data streams.

The data funnel of an embodiment temporally aligns the plurality of datastreams.

The data funnel of an embodiment spatially seams events from theplurality of data streams and generates a single synthetic event.

The data funnel of an embodiment performs semantic aggregation includingcollecting relevant events resulting from preceding operations of thedata funnel.

The data funnel of an embodiment performs metainformation tagging.

The input data of an embodiment is received from at least one of anoptical motion-tracking system, a time-of-flight tracking system, anelectric field sensing system, and a touch screen device.

The input data of an embodiment is received from an opticalmotion-tracking system.

The input data of an embodiment is received from a time-of-flighttracking system.

The input data of an embodiment is received from a touch screen device.

The input data of an embodiment is received from an electric fieldsensing system.

The input data of an embodiment is received from a capacitive sensingsystem.

The gesture engine of an embodiment receives the spatiotemporal datacomprising three-space position of the object.

The gesture engine of an embodiment receives the spatiotemporal datacomprising three-space orientation of the object.

The gesture engine of an embodiment receives the spatiotemporal datacomprising motion of the object.

The gesture engine of an embodiment receives the spatiotemporal datacomprising bulk three-space position of at least one of a plurality ofelements comprising the object and a plurality of elements coupled tothe object.

The gesture engine of an embodiment receives the spatiotemporal datacomprising bulk three-space orientation of at least one of a pluralityof elements comprising the object and a plurality of elements coupled tothe object.

The gesture engine of an embodiment receives the spatiotemporal datacomprising bulk motion of at least one of a plurality of elementscomprising the object and a plurality of elements coupled to the object.

The gesture engine of an embodiment receives the spatiotemporal datacomprising a semantic digest of a pose of a plurality of elementscomprising the object.

The gesture engine of an embodiment compares the spatiotemporal data tothe gesture descriptions.

The gesture engine of an embodiment generates the protoevent in responseto a match between spatiotemporal data and a gesture description,wherein the protoevent includes a data format comprising a digest ofmatched spatiotemporal data, interpreted in a semantic context ofmatched gestural descriptions.

The gesture engine of an embodiment comprises a plurality ofrecognizers, wherein each recognizer comprises a gesture description.

The gesture engine of an embodiment performs a plurality of operationson the plurality of recognizers, wherein the plurality of operationscomprise ranking recognizers.

The gesture engine of an embodiment performs a plurality of operationson the plurality of recognizers, wherein the plurality of operationscomprise adding recognizers.

The gesture engine of an embodiment performs a plurality of operationson the plurality of recognizers, wherein the plurality of operationscomprise removing recognizers.

The gesture engine of an embodiment performs a plurality of operationson the plurality of recognizers, wherein the plurality of operationscomprise modifying recognizers.

The gesture engine of an embodiment performs a plurality of operationson the plurality of recognizers, wherein the plurality of operationscomprise reconfiguring recognizers.

The recognizer of an embodiment remains dormant prior to a match betweenspatiotemporal data and activation criteria of the recognizer.

The recognizer of an embodiment becomes active when geometric andspatiotemporal aspects of the spatiotemporal data match the activationcriteria.

The recognizer of an embodiment remains active as long as thespatiotemporal data satisfy maintenance criteria of the recognizer.

The recognizer of an embodiment becomes inactive when the spatiotemporaldata fail to satisfy the maintenance criteria.

The distributor of an embodiment deposits the protoevents in at leastone repository for access by the at least one event consumer.

The distributor of an embodiment comprises a list of the at least oneevent consumer.

The distributor of an embodiment asynchronously transmits eachprotoevent generated by the gesture engine to each of the at least oneevent consumers.

The distributor of an embodiment synchronously transmits each protoeventgenerated by the gesture engine to each of the at least one eventconsumers.

The system of an embodiment comprises a transformer coupled to a remoteclient device of the at least one consumer, wherein the transformerre-renders the gestural events in a spatial-semantic frame of referenceof the at least one event consumer.

The distributor of an embodiment includes the transformer.

The gesture engine of an embodiment generates the protoevent bygenerating at least one data sequence comprising gestural event dataspecifying the gestural event and state information of the gesturalevent, and forming a data capsule to include the at least one datasequence, the data capsule having a data structure comprising anapplication-independent representation of the at least one datasequence.

The generating of the at least one data sequence of an embodimentcomprises generating a first respective data set that includes firstrespective gestural event data. The generating of the at least one datasequence of an embodiment comprises generating a second respective dataset that includes second respective state information. The generating ofthe at least one data sequence of an embodiment comprises forming afirst data sequence to include the first respective data set and thesecond respective data set.

The generating of the at least one data sequence of an embodimentcomprises generating a first respective data set that includes firstrespective gestural event data. The generating of the at least one datasequence of an embodiment comprises generating a second respective dataset that includes second respective state information. The generating ofthe at least one data sequence of an embodiment comprises forming asecond data sequence to include the first respective data set and thesecond respective data set.

The generating of the first respective data set of an embodimentincludes generating a first respective data set offset, wherein thefirst respective data set offset points to the first respective data setof the second data sequence.

The generating of the second respective data set of an embodimentincludes generating a second respective data set offset, wherein thesecond respective data set offset points to the second respective dataset of the second data sequence.

The first respective data set of an embodiment is a description list,the description list including a description of the data.

The system of an embodiment comprises generating at least one offset.The system of an embodiment comprises forming the data capsule toinclude the at least one offset.

The system of an embodiment comprises generating a first offset having afirst variable length. The first offset points to the gestural eventdata of a first data sequence of the at least one data sequence.

The system of an embodiment comprises generating a second offset havinga second variable length. The second offset points to the stateinformation of a first data sequence of the at least one data sequence.

The system of an embodiment comprises forming a first code path throughthe data capsule using a first offset of the at least one offset. Thesystem of an embodiment comprises forming a second code path through thedata capsule using a second offset of the at least one offset. The firstcode path and the second code path are different paths.

At least one of the first offset and the second offset of an embodimentinclude metadata, the metadata comprising context-specific metadata.

At least one event consumer of an embodiment is at least one interactivesystem of a plurality of interactive systems, wherein the plurality ofinteractive systems comprises a plurality of frames of reference.

At least one event consumer of an embodiment consumes the protoeventsusing an application type specific to the at least one event consumer.

At least one event consumer of an embodiment comprises a firstinteractive system having a first frame of reference and a secondinteractive system having a second frame of reference.

The first interactive system of an embodiment consumes the protoeventsusing a first application type and the second interactive systemconsumes the protoevents using a second application type.

The object of an embodiment is a human hand.

The object of an embodiment is at least one finger of a human hand.

The object of an embodiment includes at least one human hand and atleast one finger of a human hand.

Embodiments described herein include a system comprising a data funnelcoupled to a processor. The data funnel collates input data from aplurality of sources and conforms the input data into a spatiotemporaldata stream. The input data is absolute three-space location data of aninstantaneous state of a body at a point in time and space. The systemof an embodiment comprises a gesture engine coupled to the data funnel.The gesture engine generates gestural events from the spatiotemporaldata stream using a plurality of gesture descriptions. The gestureengine represents each gestural event in a protoevent comprising a dataformat that is application-neutral. The system of an embodimentcomprises a distributor coupled to the gesture engine. The distributorprovides access to the gestural events through access by a plurality ofevent consumers to a plurality of protoevents. The access to thegestural events is in a spatial-semantic frame of reference of theplurality of event consumers.

Embodiments described herein include a system comprising: a data funnelcoupled to a processor, wherein the data funnel collates input data froma plurality of sources and conforms the input data into a spatiotemporaldata stream, wherein the input data is absolute three-space locationdata of an instantaneous state of a body at a point in time and space; agesture engine coupled to the data funnel, wherein the gesture enginegenerates gestural events from the spatiotemporal data stream using aplurality of gesture descriptions, wherein the gesture engine representseach gestural event in a protoevent comprising a data format that isapplication-neutral; and a distributor coupled to the gesture engine,wherein the distributor provides access to the gestural events throughaccess by a plurality of event consumers to a plurality of protoevents,wherein the access to the gestural events is in a spatial-semantic frameof reference of the plurality of event consumers.

The systems and methods described herein include and/or run under and/orin association with a processing system. The processing system includesany collection of processor-based devices or computing devices operatingtogether, or components of processing systems or devices, as is known inthe art. For example, the processing system can include one or more of aportable computer, portable communication device operating in acommunication network, and/or a network server. The portable computercan be any of a number and/or combination of devices selected from amongpersonal computers, cellular telephones, personal digital assistants,portable computing devices, and portable communication devices, but isnot so limited. The processing system can include components within alarger computer system.

The processing system of an embodiment includes at least one processorand at least one memory device or subsystem. The processing system canalso include or be coupled to at least one database. The term“processor” as generally used herein refers to any logic processingunit, such as one or more central processing units (CPUs), digitalsignal processors (DSPs), application-specific integrated circuits(ASIC), etc. The processor and memory can be monolithically integratedonto a single chip, distributed among a number of chips or components ofa host system, and/or provided by some combination of algorithms. Themethods described herein can be implemented in one or more of softwarealgorithm(s), programs, firmware, hardware, components, circuitry, inany combination.

System components embodying the systems and methods described herein canbe located together or in separate locations. Consequently, systemcomponents embodying the systems and methods described herein can becomponents of a single system, multiple systems, and/or geographicallyseparate systems. These components can also be subcomponents orsubsystems of a single system, multiple systems, and/or geographicallyseparate systems. These components can be coupled to one or more othercomponents of a host system or a system coupled to the host system.

Communication paths couple the system components and include any mediumfor communicating or transferring files among the components. Thecommunication paths include wireless connections, wired connections, andhybrid wireless/wired connections. The communication paths also includecouplings or connections to networks including local area networks(LANs), metropolitan area networks (MANs), wide area networks (WANs),proprietary networks, interoffice or backend networks, and the Internet.Furthermore, the communication paths include removable fixed mediumslike floppy disks, hard disk drives, and CD-ROM disks, as well as flashRAM, Universal Serial Bus (USB) connections, RS-232 connections,telephone lines, buses, and electronic mail messages.

Unless the context clearly requires otherwise, throughout thedescription, the words “comprise,” “comprising,” and the like are to beconstrued in an inclusive sense as opposed to an exclusive or exhaustivesense; that is to say, in a sense of “including, but not limited to.”Words using the singular or plural number also include the plural orsingular number respectively. Additionally, the words “herein,”“hereunder,” “above,” “below,” and words of similar import refer to thisapplication as a whole and not to any particular portions of thisapplication. When the word “or” is used in reference to a list of two ormore items, that word covers all of the following interpretations of theword: any of the items in the list, all of the items in the list and anycombination of the items in the list.

The above description of embodiments of the processing environment isnot intended to be exhaustive or to limit the systems and methodsdescribed to the precise form disclosed. While specific embodiments of,and examples for, the processing environment are described herein forillustrative purposes, various equivalent modifications are possiblewithin the scope of other systems and methods, as those skilled in therelevant art will recognize. The teachings of the processing environmentprovided herein can be applied to other processing systems and methods,not only for the systems and methods described above.

The elements and acts of the various embodiments described above can becombined to provide further embodiments. These and other changes can bemade to the processing environment in light of the above detaileddescription.

In general, in the following claims, the terms used should not beconstrued to limit the embodiments to the specific embodiments disclosedin the specification and the claims, but should be construed to includeall systems that operate under the claims. Accordingly, the embodimentsare not limited by the disclosure herein, but instead the scope of theembodiments is to be determined entirely by the claims.

While certain aspects of the embodiments are presented below in certainclaim forms, the inventors contemplate the various aspects of theembodiments in any number of claim forms. Accordingly, the inventorsreserve the right to add additional claims after filing the applicationto pursue such additional claim forms for other aspects of theembodiments.

1. A method comprising: collating input data from a plurality ofsources, wherein the input data is semantically uncorrelated three-spacedata of an instantaneous spatial and geometric state of an object in aframe of reference of the object; conforming the input data into astream of spatiotemporal data, wherein the spatiotemporal data of thestream is uniformly represented; generating gestural events from thespatiotemporal data using a plurality of gesture descriptions;representing the gestural events in a protoevent comprising a dataformat that is application-neutral and fully articulated; anddistributing the gestural events and providing access to the gesturalevents via corresponding protoevents by at least one event consumer in aspatial-semantic frame of reference of the at least one event consumer.2. The method of claim 1, wherein the input data comprises unconstrainedfreespace gestural data of the object.
 3. The method of claim 1, whereinthe input data comprises proximal gestural data of the object when theobject is at least one of within a proximate range relative to a surfaceand within a defined volume.
 4. The method of claim 1, wherein the inputdata comprises hover gestural data of the object when the object iswithin a plane adjacent to a surface.
 5. The method of claim 1, whereinthe input data comprises surface-contact gestural data of the objectwhen the object is in contact with a surface.
 6. The method of claim 1,wherein the input data comprises a plurality of data streams.
 7. Themethod of claim 6, comprising temporally aligning the plurality of datastreams.
 8. The method of claim 6, comprising spatially seaming eventsfrom the plurality of data streams and generating a single syntheticevent.
 9. The method of claim 6, comprising performing semanticaggregation including collecting relevant events resulting frompreceding operations.
 10. The method of claim 6, comprising performingmetainformation tagging.
 11. The method of claim 6, comprising receivingthe input data from at least one of an optical motion-tracking system, atime-of-flight tracking system, an electric field sensing system, and atouch screen device. 12-23. (canceled)
 24. The method of claim 1,comprising comparing the spatiotemporal data to the gesturedescriptions.
 25. The method of claim 24, comprising generating theprotoevent in response to a match between spatiotemporal data and agesture description, wherein the protoevent includes a data formatcomprising a digest of matched spatiotemporal data, interpreted in asemantic context of matched gestural descriptions.
 26. The method ofclaim 1, comprising providing a plurality of recognizers, wherein eachrecognizer comprises a gesture description. 27-35. (canceled)
 36. Themethod of claim 1, comprising depositing the protoevents in at least onerepository for access by the at least one event consumer. 37-39.(canceled)
 40. The method of claim 1, comprising transforming thegestural events among a plurality of spatial-semantic frames ofreference corresponding to a plurality of event consumers.
 41. Themethod of claim 1, comprising re-rendering the gestural events in aspatial-semantic frame of reference of the at least one event consumer.42. The method of claim 1, comprising generating the protoevent bygenerating at least one data sequence comprising gestural event dataspecifying the gestural event and state information of the gesturalevent, and forming a data capsule to include the at least one datasequence, the data capsule having a data structure comprising anapplication-independent representation of the at least one datasequence. 43-52. (canceled)
 53. The method of claim 1, wherein the atleast one event consumer is at least one interactive system of aplurality of interactive systems, wherein the plurality of interactivesystems comprise a plurality of frames of reference. 54-59. (canceled)60. A method comprising: collating input data from a plurality ofsources, wherein the input data is semantically uncorrelated three-spacedata corresponding to an object, wherein the plurality of sourcescomprise disparate sources; rendering a plurality of spatial events ofthe object from the input data, wherein the plurality of spatial eventscomprise a conformed-coordinate representation relative to a global roomspace; generating aggregates of the spatial events from the spatialevents, wherein the aggregates are logical aggregates including literalgeometric and semantic characteristics of the object; detecting anddisambiguating gestures from the aggregates of the spatial events;generating data bundles representing the gestures, wherein the databundles are neutrally descriptive; and distributing the data bundles forconsumption by a plurality of disparate applications.